Instantaneous network model diagnostics - balanced statistics
This file shows diagnostics for instantaneous network models fit using balanced racial/ethnic mixing matrices and degree terms adjusted to correspond to the balanced mixing matrices. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.
Load packages and model fits
rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")## Loading required package: lattice## Loading required package: RColorBrewerlibrary("knitr")
library("kableExtra")
load(file = "/homes/dpwhite/R/GitHub Repos/WHAMP/Model fits and simulations/Fit tests and debugging/est/fit.i.buildup.bal.rda")Model terms and control settings
| Terms | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
|---|---|---|---|---|---|---|---|---|
| edges | 479.2 | 479.2 | 479.2 | 479.2 | 479.2 | 479.2 | 479.2 | 479.2 |
| nodefactor.deg.main.deg.pers.0.1 | NA | NA | NA | 172.3 | 172.3 | 172.3 | 172.3 | 172.3 |
| nodefactor.deg.main.deg.pers.0.2 | NA | NA | NA | 36.4 | 36.4 | 36.4 | 36.4 | 36.4 |
| nodefactor.deg.main.deg.pers.1.0 | NA | NA | NA | 38.0 | 38.0 | 38.0 | 38.0 | 38.0 |
| nodefactor.deg.main.deg.pers.1.1 | NA | NA | NA | 135.5 | 135.5 | 135.5 | 135.5 | 135.5 |
| nodefactor.deg.main.deg.pers.1.2 | NA | NA | NA | 145.4 | 145.4 | 145.4 | 145.4 | 145.4 |
| nodefactor.riskg.O1 | NA | NA | NA | NA | NA | NA | 0.4 | 0.4 |
| nodefactor.riskg.O2 | NA | NA | NA | NA | NA | NA | 0.4 | 0.4 |
| nodefactor.riskg.O3 | NA | NA | NA | NA | NA | NA | 6.9 | 6.9 |
| nodefactor.riskg.O4 | NA | NA | NA | NA | NA | NA | 109.5 | 109.5 |
| nodefactor.riskg.Y1 | NA | NA | NA | NA | NA | NA | 1.3 | 1.3 |
| nodefactor.riskg.Y2 | NA | NA | NA | NA | NA | NA | 8.2 | 8.2 |
| nodefactor.riskg.Y3 | NA | NA | NA | NA | NA | NA | 70.8 | 70.8 |
| nodefactor.race..wa.B | NA | 75.6 | 75.6 | 75.6 | 75.6 | 75.6 | 75.6 | 75.6 |
| nodefactor.race..wa.H | NA | 149.2 | 149.2 | 149.2 | 149.2 | 149.2 | 149.2 | 149.2 |
| nodefactor.region.EW | NA | NA | NA | NA | 83.5 | 83.5 | 83.5 | 83.5 |
| nodefactor.region.OW | NA | NA | NA | NA | 242.5 | 242.5 | 242.5 | 242.5 |
| nodematch.race..wa.B | NA | NA | 2.5 | 2.5 | 2.5 | 2.5 | 2.5 | 2.5 |
| nodematch.race..wa.H | NA | NA | 13.3 | 13.3 | 13.3 | 13.3 | 13.3 | 13.3 |
| nodematch.race..wa.O | NA | NA | 286.9 | 286.9 | 286.9 | 286.9 | 286.9 | 286.9 |
| nodematch.region | NA | NA | NA | NA | NA | NA | NA | 383.3 |
| absdiff.sqrt.age | NA | NA | NA | NA | NA | 380.5 | 380.5 | 380.5 |
| nodematch.role.class.I | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
| nodematch.role.class.R | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
The control settings for these models are:
set.control.ergm = control.ergm(MCMC.interval = 1e+5,
MCMC.samplesize = 7500,
MCMC.burnin = 1e+6,
MPLE.max.dyad.types = 1e+7,
MCMLE.maxit = 400,
parallel = np/2,
parallel.type="PSOCK"))MCMC diagnostics
Model 1
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## 1.1493 21.8287 0.1260 0.1264
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## -41.1586 -14.1586 0.8414 15.8414 44.8414
##
##
## Sample statistics cross-correlations:
## edges
## edges 1
##
## Sample statistics auto-correlation:
## Chain 1
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.001689893
## Lag 2e+05 0.009153440
## Lag 3e+05 0.027073547
## Lag 4e+05 -0.021024930
## Lag 5e+05 -0.021904324
## Chain 2
## edges
## Lag 0 1.000000e+00
## Lag 1e+05 -4.281928e-05
## Lag 2e+05 1.401588e-02
## Lag 3e+05 -2.204210e-02
## Lag 4e+05 -9.814745e-03
## Lag 5e+05 -8.557038e-03
## Chain 3
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.006017968
## Lag 2e+05 0.008391067
## Lag 3e+05 -0.004462612
## Lag 4e+05 0.007289324
## Lag 5e+05 -0.018245787
## Chain 4
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.003809866
## Lag 2e+05 -0.003660976
## Lag 3e+05 0.010345232
## Lag 4e+05 0.007366993
## Lag 5e+05 -0.005793656
## Chain 5
## edges
## Lag 0 1.000000e+00
## Lag 1e+05 -1.115457e-02
## Lag 2e+05 -1.289364e-02
## Lag 3e+05 5.238162e-05
## Lag 4e+05 -6.304455e-03
## Lag 5e+05 -1.903246e-02
## Chain 6
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.010499933
## Lag 2e+05 0.004685483
## Lag 3e+05 0.004444947
## Lag 4e+05 -0.020608392
## Lag 5e+05 0.005475361
## Chain 7
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.009310435
## Lag 2e+05 0.004987512
## Lag 3e+05 0.012626092
## Lag 4e+05 0.006809817
## Lag 5e+05 -0.007628960
## Chain 8
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.024097480
## Lag 2e+05 0.002007648
## Lag 3e+05 0.009428801
## Lag 4e+05 0.030299736
## Lag 5e+05 -0.020096243
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.4734
##
## Individual P-values (lower = worse):
## edges
## 0.6359181
## Joint P-value (lower = worse): 0.6339186 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -1.16
##
## Individual P-values (lower = worse):
## edges
## 0.2462308
## Joint P-value (lower = worse): 0.2423417 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.5236
##
## Individual P-values (lower = worse):
## edges
## 0.6005875
## Joint P-value (lower = worse): 0.6090205 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 1.186
##
## Individual P-values (lower = worse):
## edges
## 0.2356807
## Joint P-value (lower = worse): 0.2090867 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 1.059
##
## Individual P-values (lower = worse):
## edges
## 0.2895834
## Joint P-value (lower = worse): 0.287077 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.1135
##
## Individual P-values (lower = worse):
## edges
## 0.9096263
## Joint P-value (lower = worse): 0.9137976 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -1.107
##
## Individual P-values (lower = worse):
## edges
## 0.2683499
## Joint P-value (lower = worse): 0.2564863 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 1.397
##
## Individual P-values (lower = worse):
## edges
## 0.1622806
## Joint P-value (lower = worse): 0.1452099 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 2
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 1.5613 21.872 0.12628 0.12628
## nodefactor.race..wa.B 0.1038 8.988 0.05189 0.05186
## nodefactor.race..wa.H 0.8564 13.134 0.07583 0.07537
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -41.16 -13.159 1.8414 15.841 44.84
## nodefactor.race..wa.B -16.59 -5.591 0.4092 6.409 18.41
## nodefactor.race..wa.H -24.17 -8.174 0.8261 9.826 26.83
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.0000000 0.38441849
## nodefactor.race..wa.B 0.3844185 1.00000000
## nodefactor.race..wa.H 0.5154821 0.09685207
## nodefactor.race..wa.H
## edges 0.51548213
## nodefactor.race..wa.B 0.09685207
## nodefactor.race..wa.H 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.009022949 -0.015724055 -0.002773037
## Lag 2e+05 0.009469770 0.017596899 0.012509957
## Lag 3e+05 0.010087765 -0.007695397 -0.009495744
## Lag 4e+05 -0.039240477 -0.020216079 0.006226732
## Lag 5e+05 -0.013166712 -0.002460691 0.003743976
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0053239317 0.027962512 0.012492868
## Lag 2e+05 0.0108035530 0.009711324 0.018945139
## Lag 3e+05 0.0004870424 -0.049566267 0.016802157
## Lag 4e+05 0.0033195249 -0.021129858 -0.011135839
## Lag 5e+05 0.0248522163 0.019676352 -0.004475621
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.015922782 0.006799147 -0.026426875
## Lag 2e+05 -0.018077562 -0.009641266 -0.023913972
## Lag 3e+05 -0.016861808 -0.022274445 -0.002953678
## Lag 4e+05 0.009089872 0.014254117 0.006401110
## Lag 5e+05 -0.002920697 -0.021227343 -0.009534556
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 -0.02077977 -0.002784020 -0.014684571
## Lag 2e+05 0.01113820 0.002358376 0.006931872
## Lag 3e+05 -0.01805143 -0.008072311 0.013298663
## Lag 4e+05 0.01095108 0.006996996 -0.013841419
## Lag 5e+05 -0.02274142 0.011105677 -0.007316140
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.010753978 0.009831448 0.018405194
## Lag 2e+05 0.010881844 -0.007814431 -0.001866209
## Lag 3e+05 0.002233129 -0.009914092 0.032383537
## Lag 4e+05 -0.021256695 -0.003659570 0.014074036
## Lag 5e+05 -0.005219297 0.007280053 0.030330229
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.022106774 0.014996200 -0.009845007
## Lag 2e+05 0.014169176 -0.003223020 -0.013981472
## Lag 3e+05 0.004153907 -0.015238669 0.016785326
## Lag 4e+05 -0.014388188 -0.020144997 -0.008849356
## Lag 5e+05 -0.013637473 0.003578938 -0.020984483
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.00000000 1.0000000000 1.0000000000
## Lag 1e+05 0.01419279 -0.0000173052 0.0197091700
## Lag 2e+05 -0.01472130 0.0120946138 -0.0181859480
## Lag 3e+05 -0.01774480 0.0230643981 -0.0280144140
## Lag 4e+05 0.01012615 0.0254333911 0.0007863171
## Lag 5e+05 0.01506686 0.0124836525 0.0176464577
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.004152920 0.016560234 -0.009604462
## Lag 2e+05 -0.009282207 -0.004361675 -0.004539907
## Lag 3e+05 -0.003066974 0.012235250 0.004226071
## Lag 4e+05 0.024814229 0.026747454 -0.015784145
## Lag 5e+05 0.001001616 -0.043421193 0.007805233
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.553 1.010 1.607
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1204122 0.3126963 0.1079489
## Joint P-value (lower = worse): 0.3178617 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.31857 -0.21300 -0.03131
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7500505 0.8313296 0.9750247
## Joint P-value (lower = worse): 0.96269 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2685 1.4460 -0.6820
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7883462 0.1481834 0.4952376
## Joint P-value (lower = worse): 0.394729 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.5654 2.6781 0.6261
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.117499275 0.007404733 0.531276405
## Joint P-value (lower = worse): 0.08243981 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.7900 -0.4967 0.4714
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4295257 0.6193964 0.6373845
## Joint P-value (lower = worse): 0.6147585 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.0277 -0.1318 1.3061
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3041104 0.8951456 0.1915026
## Joint P-value (lower = worse): 0.1473041 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.2696 -0.1001 1.1351
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2042372 0.9202725 0.2563137
## Joint P-value (lower = worse): 0.4604688 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.3805 -0.8038 -1.4618
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1674296 0.4215240 0.1437899
## Joint P-value (lower = worse): 0.4158051 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 3
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 1.24218 21.854 0.126174 0.125377
## nodefactor.race..wa.B 0.10385 8.964 0.051751 0.051740
## nodefactor.race..wa.H 0.97515 13.246 0.076478 0.074811
## nodematch.race..wa.B 0.02838 1.591 0.009183 0.009184
## nodematch.race..wa.H -0.05602 3.610 0.020840 0.020637
## nodematch.race..wa.O 0.08918 16.875 0.097426 0.098370
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -41.159 -13.159 0.8414 15.841 44.841
## nodefactor.race..wa.B -16.591 -5.591 0.4092 6.409 18.409
## nodefactor.race..wa.H -24.174 -8.174 0.8261 9.826 27.826
## nodematch.race..wa.B -2.540 -1.540 -0.5399 1.460 3.460
## nodematch.race..wa.H -6.269 -2.269 -0.2690 2.731 7.731
## nodematch.race..wa.O -32.880 -11.880 0.1200 11.120 33.120
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.00000000 0.393068225
## nodefactor.race..wa.B 0.39306822 1.000000000
## nodefactor.race..wa.H 0.51386231 0.147507901
## nodematch.race..wa.B 0.06211785 0.347555220
## nodematch.race..wa.H 0.16775075 0.007447026
## nodematch.race..wa.O 0.77006663 0.004947318
## nodefactor.race..wa.H nodematch.race..wa.B
## edges 0.513862311 0.062117851
## nodefactor.race..wa.B 0.147507901 0.347555220
## nodefactor.race..wa.H 1.000000000 -0.014974825
## nodematch.race..wa.B -0.014974825 1.000000000
## nodematch.race..wa.H 0.549543147 -0.000261336
## nodematch.race..wa.O -0.006231405 0.001294147
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.1677507475 0.7700666330
## nodefactor.race..wa.B 0.0074470259 0.0049473181
## nodefactor.race..wa.H 0.5495431466 -0.0062314054
## nodematch.race..wa.B -0.0002613360 0.0012941467
## nodematch.race..wa.H 1.0000000000 -0.0007619151
## nodematch.race..wa.O -0.0007619151 1.0000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.026024014 -0.007861391 -0.018745335
## Lag 2e+05 0.010972370 -0.016940120 0.002058190
## Lag 3e+05 0.006346110 0.001170770 0.013354741
## Lag 4e+05 0.011008592 -0.015434240 0.027563978
## Lag 5e+05 -0.009772797 -0.036919251 0.008208136
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.0008005827 -0.0114541206 -0.028089462
## Lag 2e+05 -0.0128978015 0.0106560330 0.006066513
## Lag 3e+05 0.0099718233 -0.0177363375 0.012666456
## Lag 4e+05 0.0070236819 0.0096399365 0.043240094
## Lag 5e+05 0.0252195969 -0.0002301732 -0.021047261
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.013935792 0.008511563 0.017970302
## Lag 2e+05 0.021723465 0.021004695 0.028664223
## Lag 3e+05 0.012197349 -0.011175970 -0.023949698
## Lag 4e+05 -0.005521791 0.025413985 -0.011846986
## Lag 5e+05 0.031675169 0.021346591 -0.005736075
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000e+00 1.000000000
## Lag 1e+05 0.003471047 -2.423258e-02 0.007905871
## Lag 2e+05 0.011120527 1.403859e-02 -0.003365791
## Lag 3e+05 -0.008340247 -3.546904e-03 0.035037156
## Lag 4e+05 -0.021522482 9.606818e-06 -0.015396389
## Lag 5e+05 -0.014900792 -1.329879e-02 0.019795714
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.003397993 0.0060004583 -0.005675022
## Lag 2e+05 0.024716748 -0.0001688861 0.022123298
## Lag 3e+05 -0.016269318 -0.0176075680 0.021586456
## Lag 4e+05 0.011334099 0.0087533477 -0.017738368
## Lag 5e+05 0.022205606 0.0088037143 0.008110501
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.034108446 -0.023250311 0.0008044902
## Lag 2e+05 0.004047861 0.017671998 0.0393916194
## Lag 3e+05 0.009411459 0.008775821 -0.0288122799
## Lag 4e+05 0.001718397 0.010825431 0.0088465087
## Lag 5e+05 0.035321841 0.001819671 -0.0010461830
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.01899986 -0.022878875 -0.014343713
## Lag 2e+05 -0.02547010 0.022200519 -0.009866647
## Lag 3e+05 -0.02047446 -0.003285218 -0.019404079
## Lag 4e+05 -0.01556433 -0.023868140 0.015184960
## Lag 5e+05 -0.00135302 -0.012179588 0.006803242
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0208354506 -0.022366455 0.020593395
## Lag 2e+05 0.0226661605 -0.009328364 -0.019323919
## Lag 3e+05 -0.0083598465 0.013827168 -0.008600903
## Lag 4e+05 0.0221293699 0.003358433 -0.011669822
## Lag 5e+05 0.0006011164 0.029316371 -0.003626088
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.012808393 0.001862343 -0.037829167
## Lag 2e+05 -0.011224363 -0.028467306 -0.008386686
## Lag 3e+05 -0.009118353 0.001797468 -0.030248435
## Lag 4e+05 0.011517309 0.020184801 -0.006456949
## Lag 5e+05 0.010945594 0.002058734 0.005702717
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.005658357 0.005165130 0.0022019931
## Lag 2e+05 0.008933518 -0.004424192 -0.0098330136
## Lag 3e+05 0.004927673 -0.027353627 0.0075251217
## Lag 4e+05 0.005856029 -0.004294367 0.0253837951
## Lag 5e+05 0.011854049 -0.013165580 0.0004862648
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.026050767 -0.026819077 0.003480310
## Lag 2e+05 0.008923019 0.003030946 -0.003883918
## Lag 3e+05 -0.001005758 -0.028951427 0.012633259
## Lag 4e+05 0.013174808 0.019301918 -0.020416136
## Lag 5e+05 0.003431148 0.005386935 -0.028325800
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.006577972 -0.031874302 -0.010483285
## Lag 2e+05 0.006504528 0.011905147 0.007888239
## Lag 3e+05 0.013913778 0.011087834 0.002745668
## Lag 4e+05 0.012330248 -0.010013670 0.009047372
## Lag 5e+05 -0.025398403 -0.006265385 -0.017007644
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0126900790 0.023438634 0.013476453
## Lag 2e+05 -0.0115051782 -0.016919763 -0.001146344
## Lag 3e+05 0.0095044435 0.002674901 0.005898297
## Lag 4e+05 0.0067431482 0.014535407 -0.004917430
## Lag 5e+05 0.0001885166 -0.011969812 0.014887413
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.032731885 -0.0086217183 -0.005945039
## Lag 2e+05 -0.000234731 -0.0019192373 0.001236863
## Lag 3e+05 -0.003190540 -0.0003931676 0.007358085
## Lag 4e+05 -0.002904919 -0.0011007239 -0.014833666
## Lag 5e+05 -0.023471044 0.0001542197 -0.005942981
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.012009216 0.015176946 0.0012529780
## Lag 2e+05 0.007658820 -0.008131660 -0.0002199334
## Lag 3e+05 0.003108993 -0.001200120 -0.0025650908
## Lag 4e+05 0.004779292 -0.006300082 -0.0044060517
## Lag 5e+05 0.026567282 0.001779794 -0.0032720252
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.008926717 -0.0125749787 -0.002028612
## Lag 2e+05 0.025454993 -0.0244431110 -0.007979207
## Lag 3e+05 -0.013861312 0.0243782442 -0.001393803
## Lag 4e+05 -0.002014802 0.0004982371 0.025947652
## Lag 5e+05 -0.042091889 0.0002028151 0.023182211
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.2619 0.6713 -0.1615
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.2446 -1.0942 -0.5758
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7933912 0.5020141 0.8717109
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8067607 0.2738455 0.5647508
## Joint P-value (lower = worse): 0.8753228 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.02732 -0.81751 -0.91368
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.42574 0.07231 1.04802
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9782080 0.4136370 0.3608859
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.6702940 0.9423546 0.2946291
## Joint P-value (lower = worse): 0.7453654 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.5289 0.4494 0.3370
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.4421 1.0338 0.5587
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.5969072 0.6531659 0.7361159
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.6584065 0.3012362 0.5763911
## Joint P-value (lower = worse): 0.9130372 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.02998 1.21873 -0.40860
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.11535 -0.38237 -0.02810
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9760818 0.2229449 0.6828357
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.9081705 0.7021851 0.9775808
## Joint P-value (lower = worse): 0.7643018 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.1795 1.3251 0.2671
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1265 0.2028 -0.8297
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8575714 0.1851321 0.7893596
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8993374 0.8393110 0.4066920
## Joint P-value (lower = worse): 0.8283275 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.2484 -0.7663 -1.0240
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.2443 -1.8879 -0.9663
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2118744 0.4434894 0.3058226
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.2133973 0.0590455 0.3338725
## Joint P-value (lower = worse): 0.4258426 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.2676 0.5372 0.3934
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.7980 0.8157 -1.0561
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7889983 0.5911472 0.6940114
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.4248484 0.4146513 0.2909213
## Joint P-value (lower = worse): 0.5042826 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4220 0.5814 -0.2636
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.5092 -0.7183 0.7733
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6730129 0.5609594 0.7921174
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.6106132 0.4725897 0.4393279
## Joint P-value (lower = worse): 0.4797669 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 4
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.68295 22.059 0.127358 0.12699
## nodefactor.deg.main.deg.pers.0.1 -0.07174 14.379 0.083016 0.08452
## nodefactor.deg.main.deg.pers.0.2 0.20720 6.154 0.035533 0.03553
## nodefactor.deg.main.deg.pers.1.0 0.07756 6.314 0.036452 0.03769
## nodefactor.deg.main.deg.pers.1.1 0.43554 12.457 0.071921 0.07168
## nodefactor.deg.main.deg.pers.1.2 0.06271 13.043 0.075305 0.07483
## nodefactor.race..wa.B 0.08848 8.992 0.051916 0.05164
## nodefactor.race..wa.H 0.45065 13.267 0.076600 0.07794
## nodematch.race..wa.B 0.05815 1.608 0.009286 0.00928
## nodematch.race..wa.H 0.16191 3.631 0.020963 0.02088
## nodematch.race..wa.O 0.48241 17.043 0.098395 0.09899
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -42.159 -14.159 0.84138 15.841 44.841
## nodefactor.deg.main.deg.pers.0.1 -27.310 -10.310 -0.31004 9.690 28.690
## nodefactor.deg.main.deg.pers.0.2 -11.371 -4.371 -0.37103 4.629 12.629
## nodefactor.deg.main.deg.pers.1.0 -12.033 -4.033 -0.03347 3.967 12.967
## nodefactor.deg.main.deg.pers.1.1 -23.538 -8.538 0.46214 8.462 25.462
## nodefactor.deg.main.deg.pers.1.2 -24.388 -9.388 -0.38812 8.612 25.637
## nodefactor.race..wa.B -16.591 -6.591 -0.59082 6.409 18.409
## nodefactor.race..wa.H -25.174 -8.174 -0.17392 8.826 26.826
## nodematch.race..wa.B -2.540 -1.540 -0.53985 1.460 3.460
## nodematch.race..wa.H -6.269 -2.269 -0.26902 2.731 7.731
## nodematch.race..wa.O -31.880 -10.880 0.11998 12.120 34.120
##
##
## Sample statistics cross-correlations:
## edges
## edges 1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.55667565
## nodefactor.deg.main.deg.pers.0.2 0.27027438
## nodefactor.deg.main.deg.pers.1.0 0.27391605
## nodefactor.deg.main.deg.pers.1.1 0.50227563
## nodefactor.deg.main.deg.pers.1.2 0.51557211
## nodefactor.race..wa.B 0.38787191
## nodefactor.race..wa.H 0.51437157
## nodematch.race..wa.B 0.07608636
## nodematch.race..wa.H 0.16598277
## nodematch.race..wa.O 0.77665651
## nodefactor.deg.main.deg.pers.0.1
## edges 0.55667565
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.08145035
## nodefactor.deg.main.deg.pers.1.0 0.06615908
## nodefactor.deg.main.deg.pers.1.1 0.14595394
## nodefactor.deg.main.deg.pers.1.2 0.14454351
## nodefactor.race..wa.B 0.22411727
## nodefactor.race..wa.H 0.27639304
## nodematch.race..wa.B 0.04018601
## nodematch.race..wa.H 0.08330158
## nodematch.race..wa.O 0.43445295
## nodefactor.deg.main.deg.pers.0.2
## edges 0.27027438
## nodefactor.deg.main.deg.pers.0.1 0.08145035
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.03757093
## nodefactor.deg.main.deg.pers.1.1 0.06484824
## nodefactor.deg.main.deg.pers.1.2 0.07242332
## nodefactor.race..wa.B 0.09053553
## nodefactor.race..wa.H 0.12387700
## nodematch.race..wa.B 0.01228321
## nodematch.race..wa.H 0.02982323
## nodematch.race..wa.O 0.22439250
## nodefactor.deg.main.deg.pers.1.0
## edges 0.27391605
## nodefactor.deg.main.deg.pers.0.1 0.06615908
## nodefactor.deg.main.deg.pers.0.2 0.03757093
## nodefactor.deg.main.deg.pers.1.0 1.00000000
## nodefactor.deg.main.deg.pers.1.1 0.07135700
## nodefactor.deg.main.deg.pers.1.2 0.07520253
## nodefactor.race..wa.B 0.08984153
## nodefactor.race..wa.H 0.16496842
## nodematch.race..wa.B 0.00735322
## nodematch.race..wa.H 0.06412388
## nodematch.race..wa.O 0.20476023
## nodefactor.deg.main.deg.pers.1.1
## edges 0.50227563
## nodefactor.deg.main.deg.pers.0.1 0.14595394
## nodefactor.deg.main.deg.pers.0.2 0.06484824
## nodefactor.deg.main.deg.pers.1.0 0.07135700
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.13409180
## nodefactor.race..wa.B 0.16857777
## nodefactor.race..wa.H 0.27579884
## nodematch.race..wa.B 0.03705199
## nodematch.race..wa.H 0.09782362
## nodematch.race..wa.O 0.39038346
## nodefactor.deg.main.deg.pers.1.2
## edges 0.51557211
## nodefactor.deg.main.deg.pers.0.1 0.14454351
## nodefactor.deg.main.deg.pers.0.2 0.07242332
## nodefactor.deg.main.deg.pers.1.0 0.07520253
## nodefactor.deg.main.deg.pers.1.1 0.13409180
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.race..wa.B 0.17989812
## nodefactor.race..wa.H 0.31078883
## nodematch.race..wa.B 0.02961878
## nodematch.race..wa.H 0.11322877
## nodematch.race..wa.O 0.38285480
## nodefactor.race..wa.B
## edges 0.387871906
## nodefactor.deg.main.deg.pers.0.1 0.224117270
## nodefactor.deg.main.deg.pers.0.2 0.090535532
## nodefactor.deg.main.deg.pers.1.0 0.089841530
## nodefactor.deg.main.deg.pers.1.1 0.168577766
## nodefactor.deg.main.deg.pers.1.2 0.179898116
## nodefactor.race..wa.B 1.000000000
## nodefactor.race..wa.H 0.141905467
## nodematch.race..wa.B 0.349311339
## nodematch.race..wa.H -0.002678008
## nodematch.race..wa.O 0.007065495
## nodefactor.race..wa.H
## edges 0.514371569
## nodefactor.deg.main.deg.pers.0.1 0.276393039
## nodefactor.deg.main.deg.pers.0.2 0.123876998
## nodefactor.deg.main.deg.pers.1.0 0.164968425
## nodefactor.deg.main.deg.pers.1.1 0.275798837
## nodefactor.deg.main.deg.pers.1.2 0.310788826
## nodefactor.race..wa.B 0.141905467
## nodefactor.race..wa.H 1.000000000
## nodematch.race..wa.B 0.003790146
## nodematch.race..wa.H 0.551340709
## nodematch.race..wa.O 0.003291703
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.0760863630 0.1659827659
## nodefactor.deg.main.deg.pers.0.1 0.0401860130 0.0833015834
## nodefactor.deg.main.deg.pers.0.2 0.0122832142 0.0298232306
## nodefactor.deg.main.deg.pers.1.0 0.0073532203 0.0641238797
## nodefactor.deg.main.deg.pers.1.1 0.0370519944 0.0978236193
## nodefactor.deg.main.deg.pers.1.2 0.0296187846 0.1132287721
## nodefactor.race..wa.B 0.3493113387 -0.0026780076
## nodefactor.race..wa.H 0.0037901456 0.5513407095
## nodematch.race..wa.B 1.0000000000 0.0009021849
## nodematch.race..wa.H 0.0009021849 1.0000000000
## nodematch.race..wa.O 0.0038975379 -0.0001963725
## nodematch.race..wa.O
## edges 0.7766565131
## nodefactor.deg.main.deg.pers.0.1 0.4344529522
## nodefactor.deg.main.deg.pers.0.2 0.2243925038
## nodefactor.deg.main.deg.pers.1.0 0.2047602322
## nodefactor.deg.main.deg.pers.1.1 0.3903834633
## nodefactor.deg.main.deg.pers.1.2 0.3828547995
## nodefactor.race..wa.B 0.0070654945
## nodefactor.race..wa.H 0.0032917029
## nodematch.race..wa.B 0.0038975379
## nodematch.race..wa.H -0.0001963725
## nodematch.race..wa.O 1.0000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.018666330 -0.0037759378
## Lag 2e+05 0.005739188 0.0077095307
## Lag 3e+05 0.017224828 0.0003097942
## Lag 4e+05 0.022019981 0.0203778431
## Lag 5e+05 -0.024417255 -0.0312957186
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0011957233
## Lag 2e+05 -0.0004280329
## Lag 3e+05 -0.0249984898
## Lag 4e+05 0.0052368492
## Lag 5e+05 0.0066207407
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.011665434
## Lag 2e+05 0.015369456
## Lag 3e+05 -0.006222952
## Lag 4e+05 -0.010995256
## Lag 5e+05 -0.002979721
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.015761516
## Lag 2e+05 -0.021638176
## Lag 3e+05 0.007410929
## Lag 4e+05 0.008875459
## Lag 5e+05 -0.005338917
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.011218738 -0.009124160
## Lag 2e+05 -0.006965569 -0.010365712
## Lag 3e+05 -0.026602935 0.010689471
## Lag 4e+05 0.013076452 0.025843576
## Lag 5e+05 0.002117657 0.005826428
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.010467194 0.005059743 0.009542299
## Lag 2e+05 0.014719706 -0.024439009 0.005104634
## Lag 3e+05 -0.001571007 -0.004196889 0.017026564
## Lag 4e+05 -0.008995311 -0.003190505 0.002841721
## Lag 5e+05 0.001753801 0.009162285 -0.013640010
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.024741628
## Lag 2e+05 0.004465142
## Lag 3e+05 0.036456540
## Lag 4e+05 0.012480362
## Lag 5e+05 -0.028799367
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 -0.0245112596 -0.015535619
## Lag 2e+05 0.0072594061 -0.009627415
## Lag 3e+05 0.0031346889 -0.017858062
## Lag 4e+05 -0.0002524387 -0.015696317
## Lag 5e+05 0.0245905609 0.015873217
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.011760749
## Lag 2e+05 0.007954243
## Lag 3e+05 -0.035526524
## Lag 4e+05 -0.001058418
## Lag 5e+05 0.004657155
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 0.0005029939
## Lag 2e+05 -0.0319184639
## Lag 3e+05 -0.0016702182
## Lag 4e+05 0.0255552738
## Lag 5e+05 -0.0005405841
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.013445572
## Lag 2e+05 0.016961685
## Lag 3e+05 -0.018145050
## Lag 4e+05 0.004259120
## Lag 5e+05 -0.005679427
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.004962265 -0.007223569
## Lag 2e+05 0.011333563 0.008715700
## Lag 3e+05 -0.031380545 -0.007594706
## Lag 4e+05 0.005697910 0.005060803
## Lag 5e+05 -0.019587253 -0.007478605
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.032039999 -0.0330041403 0.023877789
## Lag 2e+05 -0.035907483 -0.0118240370 -0.009881347
## Lag 3e+05 -0.010585244 0.0054405855 -0.052200775
## Lag 4e+05 -0.019256209 0.0039377371 -0.011225338
## Lag 5e+05 0.003245098 0.0008000378 0.003171438
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.032921907
## Lag 2e+05 0.039025377
## Lag 3e+05 -0.003687544
## Lag 4e+05 0.015646067
## Lag 5e+05 0.011673086
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.014242898 -0.022798044
## Lag 2e+05 -0.016710212 0.015749674
## Lag 3e+05 -0.009227847 -0.006855614
## Lag 4e+05 -0.005643982 0.002270064
## Lag 5e+05 0.020141324 -0.009235859
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000e+00
## Lag 1e+05 -3.282863e-02
## Lag 2e+05 -8.720194e-05
## Lag 3e+05 -6.570127e-03
## Lag 4e+05 5.602982e-03
## Lag 5e+05 -1.402472e-02
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.011110284
## Lag 2e+05 -0.023554243
## Lag 3e+05 0.010679749
## Lag 4e+05 0.017926291
## Lag 5e+05 0.009951356
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.026839355
## Lag 2e+05 0.003480059
## Lag 3e+05 0.015853757
## Lag 4e+05 -0.003736142
## Lag 5e+05 -0.010996598
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.019843598 -0.0075693675
## Lag 2e+05 -0.031826382 0.0271848003
## Lag 3e+05 -0.001297367 -0.0061991839
## Lag 4e+05 -0.035336028 0.0163928492
## Lag 5e+05 0.005051403 0.0002235371
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.034770221 0.0073102902 0.023200344
## Lag 2e+05 0.001394111 0.0137706210 0.012008222
## Lag 3e+05 0.005598855 0.0150130511 -0.009735507
## Lag 4e+05 0.012252428 -0.0006232159 0.011196082
## Lag 5e+05 0.033866331 0.0063852287 0.012256199
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.010803411
## Lag 2e+05 -0.001649748
## Lag 3e+05 0.013520755
## Lag 4e+05 0.013075908
## Lag 5e+05 0.018760639
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 -0.0004328436 -0.003693957
## Lag 2e+05 0.0224527203 -0.021948894
## Lag 3e+05 0.0178884449 0.009722417
## Lag 4e+05 0.0015986205 0.018326052
## Lag 5e+05 -0.0014412580 -0.016342832
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.013530686
## Lag 2e+05 -0.005714144
## Lag 3e+05 0.013785313
## Lag 4e+05 0.015238548
## Lag 5e+05 0.014970668
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.00000000
## Lag 1e+05 0.02042794
## Lag 2e+05 -0.01031929
## Lag 3e+05 0.04043121
## Lag 4e+05 -0.02787066
## Lag 5e+05 -0.02764741
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.010194717
## Lag 2e+05 0.010143559
## Lag 3e+05 0.008798206
## Lag 4e+05 0.009328183
## Lag 5e+05 -0.022716616
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.023640688 -0.012497109
## Lag 2e+05 0.001897605 -0.028164082
## Lag 3e+05 0.008629031 -0.021345583
## Lag 4e+05 0.019604026 0.006250072
## Lag 5e+05 0.014309604 0.020331105
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.020061050 0.015511518 0.002648422
## Lag 2e+05 -0.014121742 -0.005763931 0.017602241
## Lag 3e+05 -0.001171554 -0.022184840 0.002914685
## Lag 4e+05 0.009695251 -0.016335285 -0.014353659
## Lag 5e+05 0.020461146 0.001655770 0.023073279
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.002048927
## Lag 2e+05 0.001303902
## Lag 3e+05 -0.006224714
## Lag 4e+05 -0.021818495
## Lag 5e+05 0.002785844
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.007375889 -0.013635213
## Lag 2e+05 0.002569697 0.002840754
## Lag 3e+05 -0.007854194 -0.018822130
## Lag 4e+05 -0.006814368 0.008843009
## Lag 5e+05 0.016207256 0.014326501
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 -0.0020985566
## Lag 2e+05 0.0331060504
## Lag 3e+05 -0.0003591816
## Lag 4e+05 -0.0042162793
## Lag 5e+05 0.0158837151
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 0.0177174264
## Lag 2e+05 0.0043742567
## Lag 3e+05 0.0141739261
## Lag 4e+05 0.0164211494
## Lag 5e+05 -0.0004675339
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000000
## Lag 1e+05 0.0008935991
## Lag 2e+05 0.0088647330
## Lag 3e+05 0.0055317967
## Lag 4e+05 0.0082241411
## Lag 5e+05 -0.0062602582
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.006773484 -0.003412259
## Lag 2e+05 0.028863504 0.001529638
## Lag 3e+05 0.015426932 -0.002066278
## Lag 4e+05 -0.035930686 -0.007378281
## Lag 5e+05 0.034783896 -0.016970582
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.029746695 -0.008705438 0.021216540
## Lag 2e+05 0.008070614 -0.005622164 -0.005283156
## Lag 3e+05 -0.007824687 -0.016565797 -0.002746903
## Lag 4e+05 0.026015403 0.025684496 0.018510554
## Lag 5e+05 0.025475139 -0.002655474 0.008665309
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.015537497
## Lag 2e+05 0.011577057
## Lag 3e+05 -0.008209481
## Lag 4e+05 -0.025734770
## Lag 5e+05 0.017956338
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.011974449 0.004156950
## Lag 2e+05 -0.011950848 -0.008760281
## Lag 3e+05 0.025998785 0.002936549
## Lag 4e+05 -0.001193861 -0.009729118
## Lag 5e+05 0.029922918 0.055354895
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.008143697
## Lag 2e+05 -0.021005816
## Lag 3e+05 0.009107715
## Lag 4e+05 0.011070285
## Lag 5e+05 0.008287489
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.00000000
## Lag 1e+05 0.02920826
## Lag 2e+05 -0.01681149
## Lag 3e+05 0.03504881
## Lag 4e+05 -0.01132099
## Lag 5e+05 -0.01747777
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.019134263
## Lag 2e+05 0.004273317
## Lag 3e+05 0.029547947
## Lag 4e+05 0.013088244
## Lag 5e+05 0.007601734
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0127845399 -0.012547352
## Lag 2e+05 0.0164301192 -0.001416912
## Lag 3e+05 0.0020997803 -0.012728526
## Lag 4e+05 0.0002528062 0.003238465
## Lag 5e+05 -0.0318521626 0.005730140
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.026912185 0.002102384 0.019439430
## Lag 2e+05 0.013584014 -0.017637383 0.002864335
## Lag 3e+05 0.032814889 -0.007272760 0.006097200
## Lag 4e+05 0.017669839 -0.030022546 0.026785752
## Lag 5e+05 0.005965472 -0.018038929 -0.002212079
## nodematch.race..wa.O
## Lag 0 1.000000e+00
## Lag 1e+05 9.096203e-05
## Lag 2e+05 -2.256691e-02
## Lag 3e+05 2.099497e-03
## Lag 4e+05 -1.550796e-02
## Lag 5e+05 2.593182e-02
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.019767150 -0.005024693
## Lag 2e+05 -0.001151465 0.029787954
## Lag 3e+05 0.005519589 0.030181888
## Lag 4e+05 -0.018130627 -0.022831583
## Lag 5e+05 0.006745333 0.017784850
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.007417689
## Lag 2e+05 -0.009973025
## Lag 3e+05 -0.019231361
## Lag 4e+05 0.004947851
## Lag 5e+05 0.008159084
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.00000000
## Lag 1e+05 -0.01156438
## Lag 2e+05 0.01630705
## Lag 3e+05 0.01560482
## Lag 4e+05 -0.01431009
## Lag 5e+05 0.03181561
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.003912581
## Lag 2e+05 0.006818850
## Lag 3e+05 0.015476021
## Lag 4e+05 -0.013224579
## Lag 5e+05 0.016211170
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 -0.039018433 -0.01279031
## Lag 2e+05 0.011111121 0.01931533
## Lag 3e+05 -0.023793937 0.02178275
## Lag 4e+05 0.030590710 -0.00103793
## Lag 5e+05 0.003704081 0.02773055
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.023009521 -0.003980853 -0.0081311029
## Lag 2e+05 -0.006578769 0.023155173 0.0074280089
## Lag 3e+05 -0.026202325 -0.008369137 0.0004053863
## Lag 4e+05 -0.003029024 -0.003426198 -0.0209832256
## Lag 5e+05 0.001231233 -0.002850688 -0.0115765579
## nodematch.race..wa.O
## Lag 0 1.00000000
## Lag 1e+05 -0.03079417
## Lag 2e+05 -0.01984096
## Lag 3e+05 0.01315799
## Lag 4e+05 -0.01119661
## Lag 5e+05 -0.00588272
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.013385862 0.002912298
## Lag 2e+05 0.006714281 0.033986965
## Lag 3e+05 -0.001097486 -0.012346889
## Lag 4e+05 0.013835183 0.010791033
## Lag 5e+05 -0.010402714 -0.015808080
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 -0.0073700831
## Lag 2e+05 0.0185889334
## Lag 3e+05 -0.0033464658
## Lag 4e+05 0.0001437143
## Lag 5e+05 0.0276944798
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 0.0017742096
## Lag 2e+05 0.0158863330
## Lag 3e+05 0.0569137145
## Lag 4e+05 -0.0006681401
## Lag 5e+05 0.0485637811
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000000
## Lag 1e+05 -0.0058893420
## Lag 2e+05 0.0108751046
## Lag 3e+05 -0.0003538409
## Lag 4e+05 -0.0119199434
## Lag 5e+05 -0.0036520360
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 -0.007245302 -0.01139455
## Lag 2e+05 -0.047578111 -0.03460990
## Lag 3e+05 0.003336066 -0.01150279
## Lag 4e+05 -0.025807617 -0.01784352
## Lag 5e+05 -0.015344533 0.02317056
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.00000000
## Lag 1e+05 0.0018551123 0.025624335 -0.01644295
## Lag 2e+05 0.0143060945 0.015216757 0.03663161
## Lag 3e+05 -0.0030124521 0.011623074 -0.04272490
## Lag 4e+05 0.0187610937 -0.006498567 0.01783758
## Lag 5e+05 -0.0008851809 0.014396257 -0.01490497
## nodematch.race..wa.O
## Lag 0 1.000000e+00
## Lag 1e+05 1.598251e-02
## Lag 2e+05 -2.234496e-03
## Lag 3e+05 1.968089e-02
## Lag 4e+05 9.085944e-05
## Lag 5e+05 -1.648389e-03
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.2535 -1.4789
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -1.2099 0.9566
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.1378 0.6199
## nodefactor.race..wa.B nodefactor.race..wa.H
## -1.6236 -0.6300
## nodematch.race..wa.B nodematch.race..wa.H
## -0.3842 0.2364
## nodematch.race..wa.O
## 0.4723
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.7999128 0.1391555
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.2263242 0.3387624
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.2552118 0.5353449
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1044644 0.5286838
## nodematch.race..wa.B nodematch.race..wa.H
## 0.7008285 0.8130885
## nodematch.race..wa.O
## 0.6367055
## Joint P-value (lower = worse): 0.4049708 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.29713 -0.51497
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.04106 1.52874
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.25266 -0.80829
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.32414 -0.67699
## nodematch.race..wa.B nodematch.race..wa.H
## 0.79681 -1.19253
## nodematch.race..wa.O
## 0.08736
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.7663650 0.6065706
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.9672476 0.1263297
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.8005308 0.4189240
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7458317 0.4984121
## nodematch.race..wa.B nodematch.race..wa.H
## 0.4255612 0.2330529
## nodematch.race..wa.O
## 0.9303840
## Joint P-value (lower = worse): 0.9138253 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -2.8839 -1.5541
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.4671 -2.2503
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.8571 -1.6660
## nodefactor.race..wa.B nodefactor.race..wa.H
## -2.8594 -1.4002
## nodematch.race..wa.B nodematch.race..wa.H
## -0.4413 0.7252
## nodematch.race..wa.O
## -1.5771
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.003927771 0.120150745
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.640448186 0.024431939
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.391397115 0.095716519
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.004244877 0.161455196
## nodematch.race..wa.B nodematch.race..wa.H
## 0.658987105 0.468331735
## nodematch.race..wa.O
## 0.114778603
## Joint P-value (lower = worse): 0.1442254 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.3431 0.6316
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.7348 -0.5716
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.7755 -1.2225
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4906 -0.8383
## nodematch.race..wa.B nodematch.race..wa.H
## 0.9599 -0.8720
## nodematch.race..wa.O
## 1.0689
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.7315430 0.5276231
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.4624842 0.5676263
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.4380729 0.2215238
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6236932 0.4018538
## nodematch.race..wa.B nodematch.race..wa.H
## 0.3371185 0.3832167
## nodematch.race..wa.O
## 0.2851043
## Joint P-value (lower = worse): 0.5681065 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.459831 -0.383115
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.306132 0.987571
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.135338 -2.092394
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.546500 -0.143853
## nodematch.race..wa.B nodematch.race..wa.H
## 0.862880 0.147163
## nodematch.race..wa.O
## -0.002346
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.64563732 0.70163461
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.75950441 0.32336277
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.89234500 0.03640331
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.58472246 0.88561681
## nodematch.race..wa.B nodematch.race..wa.H
## 0.38820332 0.88300351
## nodematch.race..wa.O
## 0.99812789
## Joint P-value (lower = worse): 0.7105389 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.9790 -0.8192
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.7815 -0.5860
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -1.5433 -1.4738
## nodefactor.race..wa.B nodefactor.race..wa.H
## -1.4121 -1.8985
## nodematch.race..wa.B nodematch.race..wa.H
## -0.7228 -1.4823
## nodematch.race..wa.O
## -0.9645
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.04781685 0.41264555
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.43453634 0.55786822
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.12276291 0.14052970
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.15792120 0.05763674
## nodematch.race..wa.B nodematch.race..wa.H
## 0.46977789 0.13826571
## nodematch.race..wa.O
## 0.33479727
## Joint P-value (lower = worse): 0.6746133 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.07547 0.78061
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -1.33163 -1.52421
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.49547 0.94359
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.41961 -0.46599
## nodematch.race..wa.B nodematch.race..wa.H
## -0.08790 0.32096
## nodematch.race..wa.O
## 0.15650
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.9398378 0.4350314
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.1829833 0.1274575
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.6202700 0.3453769
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6747689 0.6412222
## nodematch.race..wa.B nodematch.race..wa.H
## 0.9299525 0.7482406
## nodematch.race..wa.O
## 0.8756416
## Joint P-value (lower = worse): 0.5648157 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.160945 -0.001331
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.555878 1.763473
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.449557 0.041248
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.248324 -0.732046
## nodematch.race..wa.B nodematch.race..wa.H
## 0.095339 1.973955
## nodematch.race..wa.O
## 2.345236
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.24566424 0.99893785
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.57829458 0.07782072
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.14718213 0.96709836
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.80388392 0.46414025
## nodematch.race..wa.B nodematch.race..wa.H
## 0.92404567 0.04838682
## nodematch.race..wa.O
## 0.01901504
## Joint P-value (lower = worse): 0.05596303 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 5
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.91405 21.886 0.126357 0.12563
## nodefactor.deg.main.deg.pers.0.1 0.19193 14.216 0.082078 0.08158
## nodefactor.deg.main.deg.pers.0.2 0.08307 6.113 0.035294 0.03530
## nodefactor.deg.main.deg.pers.1.0 0.06006 6.234 0.035994 0.03644
## nodefactor.deg.main.deg.pers.1.1 0.30934 12.390 0.071536 0.07235
## nodefactor.deg.main.deg.pers.1.2 0.29004 13.003 0.075074 0.07490
## nodefactor.race..wa.B 0.19445 8.945 0.051645 0.05191
## nodefactor.race..wa.H 0.44045 13.326 0.076939 0.07677
## nodefactor.region.EW 0.16182 9.432 0.054456 0.05394
## nodefactor.region.OW 1.03306 17.386 0.100379 0.09973
## nodematch.race..wa.B 0.06108 1.617 0.009336 0.00936
## nodematch.race..wa.H 0.25481 3.682 0.021258 0.02110
## nodematch.race..wa.O 0.53524 16.934 0.097768 0.09837
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -41.159 -14.159 0.84138 15.841 43.841
## nodefactor.deg.main.deg.pers.0.1 -27.310 -9.310 -0.31004 9.690 28.690
## nodefactor.deg.main.deg.pers.0.2 -11.371 -4.371 -0.37103 3.629 12.629
## nodefactor.deg.main.deg.pers.1.0 -12.033 -4.033 -0.03347 3.967 12.967
## nodefactor.deg.main.deg.pers.1.1 -23.538 -8.538 0.46214 8.462 25.462
## nodefactor.deg.main.deg.pers.1.2 -24.388 -8.388 -0.38812 8.612 26.612
## nodefactor.race..wa.B -16.591 -5.591 0.40918 6.409 18.409
## nodefactor.race..wa.H -25.174 -8.174 -0.17392 9.826 26.826
## nodefactor.region.EW -17.501 -6.501 0.49862 6.499 19.499
## nodefactor.region.OW -32.486 -10.486 0.51379 12.514 35.514
## nodematch.race..wa.B -2.540 -1.540 -0.53985 1.460 3.460
## nodematch.race..wa.H -6.269 -2.269 -0.26902 2.731 7.731
## nodematch.race..wa.O -31.880 -10.880 0.11998 12.120 34.120
##
##
## Sample statistics cross-correlations:
## edges
## edges 1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.54752898
## nodefactor.deg.main.deg.pers.0.2 0.26499853
## nodefactor.deg.main.deg.pers.1.0 0.27359221
## nodefactor.deg.main.deg.pers.1.1 0.49736094
## nodefactor.deg.main.deg.pers.1.2 0.52001009
## nodefactor.race..wa.B 0.38623773
## nodefactor.race..wa.H 0.51343474
## nodefactor.region.EW 0.39093037
## nodefactor.region.OW 0.63871675
## nodematch.race..wa.B 0.07033868
## nodematch.race..wa.H 0.16620551
## nodematch.race..wa.O 0.77054936
## nodefactor.deg.main.deg.pers.0.1
## edges 0.54752898
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.06242871
## nodefactor.deg.main.deg.pers.1.0 0.06715536
## nodefactor.deg.main.deg.pers.1.1 0.13738878
## nodefactor.deg.main.deg.pers.1.2 0.13573073
## nodefactor.race..wa.B 0.22040767
## nodefactor.race..wa.H 0.26953645
## nodefactor.region.EW 0.22044325
## nodefactor.region.OW 0.36826942
## nodematch.race..wa.B 0.04501968
## nodematch.race..wa.H 0.08730881
## nodematch.race..wa.O 0.42562044
## nodefactor.deg.main.deg.pers.0.2
## edges 0.26499853
## nodefactor.deg.main.deg.pers.0.1 0.06242871
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.04057182
## nodefactor.deg.main.deg.pers.1.1 0.05911110
## nodefactor.deg.main.deg.pers.1.2 0.07490689
## nodefactor.race..wa.B 0.09578092
## nodefactor.race..wa.H 0.12258375
## nodefactor.region.EW 0.10089951
## nodefactor.region.OW 0.16886059
## nodematch.race..wa.B 0.01486575
## nodematch.race..wa.H 0.04042143
## nodematch.race..wa.O 0.21582452
## nodefactor.deg.main.deg.pers.1.0
## edges 0.27359221
## nodefactor.deg.main.deg.pers.0.1 0.06715536
## nodefactor.deg.main.deg.pers.0.2 0.04057182
## nodefactor.deg.main.deg.pers.1.0 1.00000000
## nodefactor.deg.main.deg.pers.1.1 0.07058375
## nodefactor.deg.main.deg.pers.1.2 0.07931307
## nodefactor.race..wa.B 0.09868432
## nodefactor.race..wa.H 0.16684848
## nodefactor.region.EW 0.11322054
## nodefactor.region.OW 0.15952037
## nodematch.race..wa.B 0.02309522
## nodematch.race..wa.H 0.06743486
## nodematch.race..wa.O 0.20006550
## nodefactor.deg.main.deg.pers.1.1
## edges 0.49736094
## nodefactor.deg.main.deg.pers.0.1 0.13738878
## nodefactor.deg.main.deg.pers.0.2 0.05911110
## nodefactor.deg.main.deg.pers.1.0 0.07058375
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.13178515
## nodefactor.race..wa.B 0.16762440
## nodefactor.race..wa.H 0.26995456
## nodefactor.region.EW 0.19070620
## nodefactor.region.OW 0.28144549
## nodematch.race..wa.B 0.02487680
## nodematch.race..wa.H 0.09383300
## nodematch.race..wa.O 0.38537290
## nodefactor.deg.main.deg.pers.1.2
## edges 0.52001009
## nodefactor.deg.main.deg.pers.0.1 0.13573073
## nodefactor.deg.main.deg.pers.0.2 0.07490689
## nodefactor.deg.main.deg.pers.1.0 0.07931307
## nodefactor.deg.main.deg.pers.1.1 0.13178515
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.race..wa.B 0.17558072
## nodefactor.race..wa.H 0.31003375
## nodefactor.region.EW 0.21361980
## nodefactor.region.OW 0.30473851
## nodematch.race..wa.B 0.02793614
## nodematch.race..wa.H 0.10590871
## nodematch.race..wa.O 0.38566749
## nodefactor.race..wa.B
## edges 0.3862377318
## nodefactor.deg.main.deg.pers.0.1 0.2204076747
## nodefactor.deg.main.deg.pers.0.2 0.0957809199
## nodefactor.deg.main.deg.pers.1.0 0.0986843166
## nodefactor.deg.main.deg.pers.1.1 0.1676244030
## nodefactor.deg.main.deg.pers.1.2 0.1755807179
## nodefactor.race..wa.B 1.0000000000
## nodefactor.race..wa.H 0.1445119702
## nodefactor.region.EW 0.1052166444
## nodefactor.region.OW 0.2296831011
## nodematch.race..wa.B 0.3554556305
## nodematch.race..wa.H -0.0002008646
## nodematch.race..wa.O 0.0001534158
## nodefactor.race..wa.H
## edges 0.513434743
## nodefactor.deg.main.deg.pers.0.1 0.269536452
## nodefactor.deg.main.deg.pers.0.2 0.122583746
## nodefactor.deg.main.deg.pers.1.0 0.166848483
## nodefactor.deg.main.deg.pers.1.1 0.269954558
## nodefactor.deg.main.deg.pers.1.2 0.310033753
## nodefactor.race..wa.B 0.144511970
## nodefactor.race..wa.H 1.000000000
## nodefactor.region.EW 0.310654890
## nodefactor.region.OW 0.317411839
## nodematch.race..wa.B -0.003914832
## nodematch.race..wa.H 0.552472003
## nodematch.race..wa.O -0.007784974
## nodefactor.region.EW nodefactor.region.OW
## edges 0.3909303685 0.63871675
## nodefactor.deg.main.deg.pers.0.1 0.2204432536 0.36826942
## nodefactor.deg.main.deg.pers.0.2 0.1008995097 0.16886059
## nodefactor.deg.main.deg.pers.1.0 0.1132205442 0.15952037
## nodefactor.deg.main.deg.pers.1.1 0.1907062043 0.28144549
## nodefactor.deg.main.deg.pers.1.2 0.2136197968 0.30473851
## nodefactor.race..wa.B 0.1052166444 0.22968310
## nodefactor.race..wa.H 0.3106548898 0.31741184
## nodefactor.region.EW 1.0000000000 0.12809479
## nodefactor.region.OW 0.1280947937 1.00000000
## nodematch.race..wa.B -0.0004483979 0.03845368
## nodematch.race..wa.H 0.1326972437 0.10048030
## nodematch.race..wa.O 0.2555127385 0.50355447
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.0703386817 0.1662055110
## nodefactor.deg.main.deg.pers.0.1 0.0450196817 0.0873088087
## nodefactor.deg.main.deg.pers.0.2 0.0148657498 0.0404214266
## nodefactor.deg.main.deg.pers.1.0 0.0230952152 0.0674348557
## nodefactor.deg.main.deg.pers.1.1 0.0248768026 0.0938330018
## nodefactor.deg.main.deg.pers.1.2 0.0279361356 0.1059087129
## nodefactor.race..wa.B 0.3554556305 -0.0002008646
## nodefactor.race..wa.H -0.0039148316 0.5524720034
## nodefactor.region.EW -0.0004483979 0.1326972437
## nodefactor.region.OW 0.0384536811 0.1004803001
## nodematch.race..wa.B 1.0000000000 -0.0067612089
## nodematch.race..wa.H -0.0067612089 1.0000000000
## nodematch.race..wa.O 0.0006397890 -0.0035520425
## nodematch.race..wa.O
## edges 0.7705493579
## nodefactor.deg.main.deg.pers.0.1 0.4256204360
## nodefactor.deg.main.deg.pers.0.2 0.2158245181
## nodefactor.deg.main.deg.pers.1.0 0.2000654963
## nodefactor.deg.main.deg.pers.1.1 0.3853729035
## nodefactor.deg.main.deg.pers.1.2 0.3856674919
## nodefactor.race..wa.B 0.0001534158
## nodefactor.race..wa.H -0.0077849739
## nodefactor.region.EW 0.2555127385
## nodefactor.region.OW 0.5035544662
## nodematch.race..wa.B 0.0006397890
## nodematch.race..wa.H -0.0035520425
## nodematch.race..wa.O 1.0000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.006857364 0.004856506
## Lag 2e+05 0.003910362 0.003024641
## Lag 3e+05 0.019707107 0.008965933
## Lag 4e+05 0.023564105 -0.016258853
## Lag 5e+05 -0.009774674 -0.005776247
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.001243093
## Lag 2e+05 -0.025208849
## Lag 3e+05 0.009095668
## Lag 4e+05 0.016170421
## Lag 5e+05 -0.019582054
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.003885390
## Lag 2e+05 0.032108561
## Lag 3e+05 -0.011630055
## Lag 4e+05 0.036018473
## Lag 5e+05 0.003527715
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.003968618
## Lag 2e+05 0.008028347
## Lag 3e+05 -0.026856848
## Lag 4e+05 -0.003109575
## Lag 5e+05 -0.037110955
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.020796567 0.004664558
## Lag 2e+05 0.024630196 0.028857555
## Lag 3e+05 0.008546112 0.003628222
## Lag 4e+05 0.040971677 -0.001177519
## Lag 5e+05 0.010688776 -0.003249886
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 -0.024390153 0.01816161 0.016278356
## Lag 2e+05 0.004583050 0.01039366 0.013823418
## Lag 3e+05 0.030432924 0.01329248 0.035599604
## Lag 4e+05 0.005322091 0.02424222 -0.005025381
## Lag 5e+05 -0.035730246 -0.01244155 0.003955380
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.015777705 -0.002367388 0.005550079
## Lag 2e+05 0.032489559 0.014289877 -0.003035556
## Lag 3e+05 0.031277237 -0.010899733 0.009592418
## Lag 4e+05 -0.019487498 0.021113497 0.021344585
## Lag 5e+05 -0.008853964 -0.019691787 0.007987119
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.01103306 0.030050914
## Lag 2e+05 0.01100291 -0.013990114
## Lag 3e+05 -0.01342109 -0.001859018
## Lag 4e+05 -0.04684690 -0.025624504
## Lag 5e+05 0.02131710 0.019840955
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.015124228
## Lag 2e+05 -0.009721158
## Lag 3e+05 -0.005915169
## Lag 4e+05 0.007478852
## Lag 5e+05 0.007019354
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.00000000
## Lag 1e+05 -0.01223543
## Lag 2e+05 0.00563004
## Lag 3e+05 -0.01670851
## Lag 4e+05 0.00132646
## Lag 5e+05 -0.01436945
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000e+00
## Lag 1e+05 -1.438197e-05
## Lag 2e+05 2.328759e-03
## Lag 3e+05 -3.178101e-03
## Lag 4e+05 -1.689793e-02
## Lag 5e+05 2.015815e-02
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.02727398 0.016943241
## Lag 2e+05 0.01107867 -0.027627352
## Lag 3e+05 -0.02027872 -0.002972669
## Lag 4e+05 -0.03987154 -0.001929146
## Lag 5e+05 -0.01734441 -0.012216336
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.005849324 -0.0336183662 -0.012222922
## Lag 2e+05 0.012244857 -0.0192988767 -0.006419914
## Lag 3e+05 -0.012706844 -0.0266849443 -0.011409515
## Lag 4e+05 -0.007361600 0.0209558632 -0.033607673
## Lag 5e+05 0.015799440 -0.0005670759 0.025182569
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.008263549 -0.004677559 -0.002261754
## Lag 2e+05 -0.008954718 -0.030364122 0.011965965
## Lag 3e+05 0.020774504 -0.028227253 -0.008709123
## Lag 4e+05 0.010737153 0.004341368 -0.042021758
## Lag 5e+05 0.019375801 -0.002639466 0.015483588
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 -0.0137887354 -0.006275565
## Lag 2e+05 -0.0309882766 0.015693623
## Lag 3e+05 0.0004595795 0.005969373
## Lag 4e+05 0.0058862466 -0.007628838
## Lag 5e+05 0.0184126137 0.007044716
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.021287740
## Lag 2e+05 -0.017500913
## Lag 3e+05 -0.004985468
## Lag 4e+05 -0.012773376
## Lag 5e+05 -0.002975086
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.003140334
## Lag 2e+05 0.008167299
## Lag 3e+05 0.031695904
## Lag 4e+05 0.007523533
## Lag 5e+05 -0.006099698
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.034172164
## Lag 2e+05 0.010750652
## Lag 3e+05 -0.032653070
## Lag 4e+05 -0.007238603
## Lag 5e+05 0.022498197
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.008210394 -0.014679981
## Lag 2e+05 -0.016933890 -0.038816859
## Lag 3e+05 0.006111650 -0.009224949
## Lag 4e+05 0.008504333 0.007392837
## Lag 5e+05 0.024707273 -0.008992431
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.013743185 0.004700272 -0.0307547364
## Lag 2e+05 0.021196421 0.019984168 -0.0188764340
## Lag 3e+05 -0.025633209 0.016863097 -0.0066540694
## Lag 4e+05 -0.015041360 -0.018054944 0.0008458691
## Lag 5e+05 0.007608404 -0.012619412 0.0355958740
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.004884774 0.0071005579 -0.0005501781
## Lag 2e+05 -0.012832593 -0.0005447563 -0.0216348828
## Lag 3e+05 0.004068963 -0.0045517133 0.0211320714
## Lag 4e+05 0.004570705 -0.0332450469 0.0102560900
## Lag 5e+05 -0.006564571 -0.0049544197 -0.0008227577
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000e+00
## Lag 1e+05 -0.020464738 -6.253281e-04
## Lag 2e+05 -0.009362570 -3.186040e-03
## Lag 3e+05 0.005770767 6.158326e-05
## Lag 4e+05 -0.009680727 8.146496e-03
## Lag 5e+05 -0.007748854 -2.433249e-02
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.012199504
## Lag 2e+05 0.015552600
## Lag 3e+05 0.024935582
## Lag 4e+05 -0.003207807
## Lag 5e+05 -0.007248129
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.011169611
## Lag 2e+05 -0.009397993
## Lag 3e+05 -0.005013279
## Lag 4e+05 0.020268019
## Lag 5e+05 -0.001494078
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 -0.01841948
## Lag 2e+05 -0.01384538
## Lag 3e+05 0.02514384
## Lag 4e+05 -0.01995305
## Lag 5e+05 0.01498998
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 -0.0164954527 0.002508785
## Lag 2e+05 -0.0005459395 -0.026589501
## Lag 3e+05 0.0289972064 -0.006597155
## Lag 4e+05 0.0077954276 -0.006253555
## Lag 5e+05 -0.0235217761 0.017973726
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 -0.013831132 0.00263852 -0.016433263
## Lag 2e+05 -0.009871287 -0.02214665 0.001280443
## Lag 3e+05 0.016507360 0.01258085 -0.018541046
## Lag 4e+05 -0.013388414 -0.01955355 0.023308162
## Lag 5e+05 -0.001465236 -0.01065396 0.005177430
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.018130307 0.008606582 -0.018605715
## Lag 2e+05 -0.005300516 -0.012532649 0.001542252
## Lag 3e+05 -0.002766764 -0.019751599 0.019215660
## Lag 4e+05 0.018498135 -0.003186467 -0.019566126
## Lag 5e+05 -0.008964608 -0.013422580 0.014029447
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.010544522 0.0004470325
## Lag 2e+05 0.023665393 0.0178779349
## Lag 3e+05 -0.004250223 -0.0078896270
## Lag 4e+05 -0.005547761 -0.0113721532
## Lag 5e+05 -0.029210991 -0.0220004551
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.011774685
## Lag 2e+05 0.017981476
## Lag 3e+05 -0.006461500
## Lag 4e+05 -0.007100744
## Lag 5e+05 0.014499355
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.002786609
## Lag 2e+05 0.025929956
## Lag 3e+05 -0.003301308
## Lag 4e+05 -0.019235472
## Lag 5e+05 -0.012396865
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000000
## Lag 1e+05 0.0021087087
## Lag 2e+05 -0.0081523743
## Lag 3e+05 -0.0029766389
## Lag 4e+05 0.0005692866
## Lag 5e+05 0.0004550819
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.004664382 -0.004954394
## Lag 2e+05 0.007622073 -0.014802400
## Lag 3e+05 0.008512276 -0.004963595
## Lag 4e+05 0.013274620 0.000377193
## Lag 5e+05 -0.040767013 -0.008961745
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.020846860 0.016454831 -0.023028462
## Lag 2e+05 0.019246724 0.003531526 -0.005432976
## Lag 3e+05 -0.001683569 -0.006295849 0.016781211
## Lag 4e+05 -0.028576401 -0.001690049 -0.030489143
## Lag 5e+05 0.003302175 -0.013912959 -0.007981099
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.0190454343 -0.0029653229 0.0136351622
## Lag 2e+05 -0.0213980771 0.0035440512 0.0333809175
## Lag 3e+05 0.0001292385 0.0098815139 0.0043772468
## Lag 4e+05 0.0101747588 -0.0003639628 0.0065746060
## Lag 5e+05 -0.0223436420 -0.0005130279 0.0004111833
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.022130828 -0.019921715
## Lag 2e+05 0.003471658 0.013803928
## Lag 3e+05 0.015851038 0.026784032
## Lag 4e+05 -0.011203800 0.010803650
## Lag 5e+05 0.003664508 0.005460753
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.001164807
## Lag 2e+05 0.017517501
## Lag 3e+05 -0.004993148
## Lag 4e+05 -0.001479688
## Lag 5e+05 0.012585743
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.023643366
## Lag 2e+05 -0.023637970
## Lag 3e+05 -0.006503763
## Lag 4e+05 0.007789211
## Lag 5e+05 0.040536898
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.005794186
## Lag 2e+05 0.008858028
## Lag 3e+05 -0.018278868
## Lag 4e+05 0.013501888
## Lag 5e+05 -0.008546801
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.005809032 0.003121355
## Lag 2e+05 -0.001690090 -0.012198407
## Lag 3e+05 0.014073197 0.045017733
## Lag 4e+05 -0.041903662 0.012917452
## Lag 5e+05 -0.002125754 0.041270097
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.011752168 0.021416780 0.031318722
## Lag 2e+05 -0.010429770 0.012227009 -0.015316584
## Lag 3e+05 -0.010077544 0.007280407 0.002831317
## Lag 4e+05 -0.008879258 0.004484113 0.021719733
## Lag 5e+05 -0.001821152 0.014454137 0.008184565
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.024378104 -0.0048389952 0.005085264
## Lag 2e+05 -0.005922276 -0.0053521936 0.012635583
## Lag 3e+05 0.006139092 -0.0009187231 -0.005351015
## Lag 4e+05 0.021050639 0.0087315738 -0.013625583
## Lag 5e+05 0.005171875 0.0129355261 0.001150354
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0053506718 -0.008549794
## Lag 2e+05 0.0325961982 -0.023383586
## Lag 3e+05 -0.0513304168 0.024653615
## Lag 4e+05 0.0007998579 -0.018373895
## Lag 5e+05 -0.0119199314 0.017758533
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 -0.0008693172
## Lag 2e+05 0.0119537519
## Lag 3e+05 0.0103291430
## Lag 4e+05 0.0070213953
## Lag 5e+05 0.0432135536
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.018885117
## Lag 2e+05 -0.006405783
## Lag 3e+05 -0.029358998
## Lag 4e+05 -0.016469087
## Lag 5e+05 -0.005116210
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.012930175
## Lag 2e+05 -0.004270402
## Lag 3e+05 0.024594851
## Lag 4e+05 0.021650277
## Lag 5e+05 -0.008130142
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0214626175 -0.001212405
## Lag 2e+05 0.0007869568 0.020798041
## Lag 3e+05 0.0129907964 0.001062777
## Lag 4e+05 -0.0179731678 -0.018889576
## Lag 5e+05 -0.0211796076 -0.035001842
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.009997464 -0.016408030 -0.026255077
## Lag 2e+05 0.025232727 0.006041130 0.015755153
## Lag 3e+05 -0.021333690 0.003597474 -0.039519446
## Lag 4e+05 0.017557557 0.009019426 -0.030965197
## Lag 5e+05 -0.022972694 -0.008296280 -0.000610623
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 -0.03350547 0.014448058 0.003325548
## Lag 2e+05 0.01188963 0.014613867 0.003619067
## Lag 3e+05 -0.00288365 -0.005028990 -0.037624988
## Lag 4e+05 0.01992802 0.002139552 0.005993464
## Lag 5e+05 0.01143258 0.007414210 -0.004064157
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.0000000000
## Lag 1e+05 -0.0192109268 -0.0006549314
## Lag 2e+05 0.0230679659 0.0198172762
## Lag 3e+05 0.0328499342 0.0284685902
## Lag 4e+05 -0.0147950006 0.0026511771
## Lag 5e+05 -0.0005013109 0.0044660099
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0033845984
## Lag 2e+05 0.0064923159
## Lag 3e+05 -0.0151251577
## Lag 4e+05 -0.0015935392
## Lag 5e+05 -0.0004746862
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.013928972
## Lag 2e+05 0.010156539
## Lag 3e+05 -0.005966959
## Lag 4e+05 -0.024452593
## Lag 5e+05 0.011489783
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.005413147
## Lag 2e+05 0.031953192
## Lag 3e+05 0.030720649
## Lag 4e+05 0.009788863
## Lag 5e+05 0.011431497
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000e+00
## Lag 1e+05 0.016433710 1.564407e-03
## Lag 2e+05 0.002202676 -1.377610e-02
## Lag 3e+05 -0.011635197 7.968677e-05
## Lag 4e+05 0.019459854 -3.384470e-02
## Lag 5e+05 -0.043379572 1.961191e-02
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.005394442 -0.007797414 -0.0273184186
## Lag 2e+05 0.013153283 0.020686052 0.0456855922
## Lag 3e+05 -0.002493317 0.004274311 -0.0008313697
## Lag 4e+05 0.008795834 -0.012149636 -0.0201929178
## Lag 5e+05 -0.019167059 0.001168394 -0.0089929778
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0020925684 0.004361075 -0.011182219
## Lag 2e+05 -0.0025903434 -0.008417035 0.016871717
## Lag 3e+05 -0.0172229544 0.028282203 0.026828334
## Lag 4e+05 0.0002033027 -0.010111699 -0.009326259
## Lag 5e+05 0.0070885836 0.007134567 -0.005470355
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.28400 -1.98562
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.85300 0.05557
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.08664 -0.17973
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.06293 -0.56069
## nodefactor.region.EW nodefactor.region.OW
## 0.77017 -1.56959
## nodematch.race..wa.B nodematch.race..wa.H
## 0.34292 1.58370
## nodematch.race..wa.O
## -1.00752
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.19914023 0.04707583
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.39365874 0.95568564
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.93095484 0.85736111
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.94981847 0.57500876
## nodefactor.region.EW nodefactor.region.OW
## 0.44120119 0.11650971
## nodematch.race..wa.B nodematch.race..wa.H
## 0.73165651 0.11326283
## nodematch.race..wa.O
## 0.31368377
## Joint P-value (lower = worse): 0.9746578 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 2.1123 0.6056
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.1187 0.8533
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.1805 2.1192
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1126 2.5205
## nodefactor.region.EW nodefactor.region.OW
## 0.9237 0.6115
## nodematch.race..wa.B nodematch.race..wa.H
## -0.6631 0.9490
## nodematch.race..wa.O
## 0.8955
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.03466245 0.54478611
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.90549794 0.39351329
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.23780048 0.03407294
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.91035207 0.01171722
## nodefactor.region.EW nodefactor.region.OW
## 0.35565955 0.54085412
## nodematch.race..wa.B nodematch.race..wa.H
## 0.50723524 0.34261286
## nodematch.race..wa.O
## 0.37052031
## Joint P-value (lower = worse): 0.6680909 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 2.0044 1.9741
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.3879 0.5096
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.7513 0.6653
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6237 1.1297
## nodefactor.region.EW nodefactor.region.OW
## 0.7249 1.8094
## nodematch.race..wa.B nodematch.race..wa.H
## -0.3716 0.0459
## nodematch.race..wa.O
## 1.2838
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.04502976 0.04836632
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.69809975 0.61035554
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.45248779 0.50587722
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.53282243 0.25861101
## nodefactor.region.EW nodefactor.region.OW
## 0.46849120 0.07039518
## nodematch.race..wa.B nodematch.race..wa.H
## 0.71021271 0.96339004
## nodematch.race..wa.O
## 0.19920006
## Joint P-value (lower = worse): 0.7044862 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.98502 0.33767
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.24584 1.12029
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.80087 1.34148
## nodefactor.race..wa.B nodefactor.race..wa.H
## 2.74139 -0.19803
## nodefactor.region.EW nodefactor.region.OW
## 2.11492 -0.48622
## nodematch.race..wa.B nodematch.race..wa.H
## 1.00097 -0.03204
## nodematch.race..wa.O
## 0.36911
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.324616473 0.735611469
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.212821391 0.262589464
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.423209582 0.179763139
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.006117953 0.843022093
## nodefactor.region.EW nodefactor.region.OW
## 0.034436809 0.626808299
## nodematch.race..wa.B nodematch.race..wa.H
## 0.316843552 0.974444119
## nodematch.race..wa.O
## 0.712047173
## Joint P-value (lower = worse): 0.2587543 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.9640 1.7352
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.6423 1.4519
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.7724 -1.2816
## nodefactor.race..wa.B nodefactor.race..wa.H
## 2.6847 1.1356
## nodefactor.region.EW nodefactor.region.OW
## 0.9013 0.5564
## nodematch.race..wa.B nodematch.race..wa.H
## 0.8261 0.9520
## nodematch.race..wa.O
## -0.6304
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.335055866 0.082697018
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.520705664 0.146538037
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.439858252 0.199978477
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.007260082 0.256127961
## nodefactor.region.EW nodefactor.region.OW
## 0.367405843 0.577945375
## nodematch.race..wa.B nodematch.race..wa.H
## 0.408775330 0.341087260
## nodematch.race..wa.O
## 0.528444679
## Joint P-value (lower = worse): 0.618702 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.80761 0.12787
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 2.60278 -0.83435
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.36362 0.22635
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.74752 -0.38715
## nodefactor.region.EW nodefactor.region.OW
## -0.01366 0.34029
## nodematch.race..wa.B nodematch.race..wa.H
## 0.19344 0.13808
## nodematch.race..wa.O
## 0.78401
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.419317830 0.898251407
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.009247115 0.404085545
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.716140498 0.820932119
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.454749677 0.698641638
## nodefactor.region.EW nodefactor.region.OW
## 0.989102710 0.733636389
## nodematch.race..wa.B nodematch.race..wa.H
## 0.846614009 0.890179282
## nodematch.race..wa.O
## 0.433031625
## Joint P-value (lower = worse): 0.6633008 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.9211 -0.6865
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -1.8034 -0.2070
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.4554 -0.9236
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2111 -1.8484
## nodefactor.region.EW nodefactor.region.OW
## -1.7003 -0.5093
## nodematch.race..wa.B nodematch.race..wa.H
## 0.7175 -1.4255
## nodematch.race..wa.O
## 0.2140
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.35699124 0.49239125
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.07132439 0.83599887
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.64880409 0.35568550
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.83281523 0.06454072
## nodefactor.region.EW nodefactor.region.OW
## 0.08906651 0.61056544
## nodematch.race..wa.B nodematch.race..wa.H
## 0.47305419 0.15400544
## nodematch.race..wa.O
## 0.83051321
## Joint P-value (lower = worse): 0.6960165 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.349094 0.141152
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.002975 -1.176087
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -1.543301 -0.039222
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.221457 0.925873
## nodefactor.region.EW nodefactor.region.OW
## 1.533879 -0.492842
## nodematch.race..wa.B nodematch.race..wa.H
## 1.379798 1.379718
## nodematch.race..wa.O
## -0.515833
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.7270189 0.8877502
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.9976263 0.2395600
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.1227577 0.9687133
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8247368 0.3545120
## nodefactor.region.EW nodefactor.region.OW
## 0.1250594 0.6221243
## nodematch.race..wa.B nodematch.race..wa.H
## 0.1676487 0.1676735
## nodematch.race..wa.O
## 0.6059710
## Joint P-value (lower = worse): 0.7523096 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 6
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.76761 21.644 0.124964 0.126295
## nodefactor.deg.main.deg.pers.0.1 0.55069 14.263 0.082345 0.083735
## nodefactor.deg.main.deg.pers.0.2 0.18373 6.155 0.035534 0.035849
## nodefactor.deg.main.deg.pers.1.0 0.29363 6.290 0.036315 0.036135
## nodefactor.deg.main.deg.pers.1.1 -0.11783 12.357 0.071345 0.071803
## nodefactor.deg.main.deg.pers.1.2 0.04348 12.861 0.074255 0.074425
## nodefactor.race..wa.B 0.06768 8.950 0.051670 0.052025
## nodefactor.race..wa.H 0.48585 13.202 0.076224 0.077635
## nodefactor.region.EW 0.19799 9.588 0.055355 0.055776
## nodefactor.region.OW 0.50676 17.363 0.100246 0.100236
## nodematch.race..wa.B 0.03428 1.599 0.009233 0.009288
## nodematch.race..wa.H 0.06688 3.640 0.021016 0.021318
## nodematch.race..wa.O 0.29794 16.817 0.097093 0.096259
## absdiff.sqrt.age 0.71008 22.482 0.129803 0.128788
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -41.159 -14.159 0.84138 14.841 43.841
## nodefactor.deg.main.deg.pers.0.1 -26.310 -9.310 0.68996 9.690 29.690
## nodefactor.deg.main.deg.pers.0.2 -11.371 -4.371 -0.37103 4.629 12.629
## nodefactor.deg.main.deg.pers.1.0 -11.033 -4.033 -0.03347 3.967 12.967
## nodefactor.deg.main.deg.pers.1.1 -23.538 -8.538 -0.53786 8.462 24.462
## nodefactor.deg.main.deg.pers.1.2 -24.388 -8.388 -0.38812 8.612 25.612
## nodefactor.race..wa.B -16.591 -5.591 -0.59082 6.409 17.409
## nodefactor.race..wa.H -25.174 -8.174 0.82608 8.826 26.826
## nodefactor.region.EW -17.501 -6.501 -0.50138 6.499 19.499
## nodefactor.region.OW -32.486 -11.486 0.51379 11.514 35.514
## nodematch.race..wa.B -2.540 -1.540 -0.53985 1.460 3.460
## nodematch.race..wa.H -6.269 -2.269 -0.26902 2.731 7.731
## nodematch.race..wa.O -31.880 -10.880 0.11998 11.120 33.120
## absdiff.sqrt.age -41.955 -14.564 0.20155 15.693 45.578
##
##
## Sample statistics cross-correlations:
## edges
## edges 1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.54949633
## nodefactor.deg.main.deg.pers.0.2 0.26870330
## nodefactor.deg.main.deg.pers.1.0 0.27720480
## nodefactor.deg.main.deg.pers.1.1 0.48865067
## nodefactor.deg.main.deg.pers.1.2 0.49910161
## nodefactor.race..wa.B 0.38359807
## nodefactor.race..wa.H 0.50837573
## nodefactor.region.EW 0.39823200
## nodefactor.region.OW 0.62722088
## nodematch.race..wa.B 0.07450976
## nodematch.race..wa.H 0.15432047
## nodematch.race..wa.O 0.76900448
## absdiff.sqrt.age 0.76230908
## nodefactor.deg.main.deg.pers.0.1
## edges 0.54949633
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.07280577
## nodefactor.deg.main.deg.pers.1.0 0.07747959
## nodefactor.deg.main.deg.pers.1.1 0.12926714
## nodefactor.deg.main.deg.pers.1.2 0.13570190
## nodefactor.race..wa.B 0.21236310
## nodefactor.race..wa.H 0.26429245
## nodefactor.region.EW 0.22256580
## nodefactor.region.OW 0.36280130
## nodematch.race..wa.B 0.04304162
## nodematch.race..wa.H 0.07087935
## nodematch.race..wa.O 0.42869286
## absdiff.sqrt.age 0.42057694
## nodefactor.deg.main.deg.pers.0.2
## edges 0.26870330
## nodefactor.deg.main.deg.pers.0.1 0.07280577
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.02478339
## nodefactor.deg.main.deg.pers.1.1 0.05847135
## nodefactor.deg.main.deg.pers.1.2 0.06682737
## nodefactor.race..wa.B 0.09787326
## nodefactor.race..wa.H 0.12875233
## nodefactor.region.EW 0.10002281
## nodefactor.region.OW 0.17357545
## nodematch.race..wa.B 0.01728442
## nodematch.race..wa.H 0.02937076
## nodematch.race..wa.O 0.21242386
## absdiff.sqrt.age 0.20957172
## nodefactor.deg.main.deg.pers.1.0
## edges 0.27720480
## nodefactor.deg.main.deg.pers.0.1 0.07747959
## nodefactor.deg.main.deg.pers.0.2 0.02478339
## nodefactor.deg.main.deg.pers.1.0 1.00000000
## nodefactor.deg.main.deg.pers.1.1 0.06936366
## nodefactor.deg.main.deg.pers.1.2 0.05861509
## nodefactor.race..wa.B 0.09407941
## nodefactor.race..wa.H 0.15337507
## nodefactor.region.EW 0.10632333
## nodefactor.region.OW 0.15997076
## nodematch.race..wa.B 0.01532991
## nodematch.race..wa.H 0.05917566
## nodematch.race..wa.O 0.21109428
## absdiff.sqrt.age 0.20802986
## nodefactor.deg.main.deg.pers.1.1
## edges 0.48865067
## nodefactor.deg.main.deg.pers.0.1 0.12926714
## nodefactor.deg.main.deg.pers.0.2 0.05847135
## nodefactor.deg.main.deg.pers.1.0 0.06936366
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.11149985
## nodefactor.race..wa.B 0.15627399
## nodefactor.race..wa.H 0.26534912
## nodefactor.region.EW 0.19387240
## nodefactor.region.OW 0.27184236
## nodematch.race..wa.B 0.02173968
## nodematch.race..wa.H 0.09168910
## nodematch.race..wa.O 0.37777830
## absdiff.sqrt.age 0.37003475
## nodefactor.deg.main.deg.pers.1.2
## edges 0.49910161
## nodefactor.deg.main.deg.pers.0.1 0.13570190
## nodefactor.deg.main.deg.pers.0.2 0.06682737
## nodefactor.deg.main.deg.pers.1.0 0.05861509
## nodefactor.deg.main.deg.pers.1.1 0.11149985
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.race..wa.B 0.16516477
## nodefactor.race..wa.H 0.30780167
## nodefactor.region.EW 0.21869388
## nodefactor.region.OW 0.28353425
## nodematch.race..wa.B 0.02371209
## nodematch.race..wa.H 0.10508352
## nodematch.race..wa.O 0.36353772
## absdiff.sqrt.age 0.38453726
## nodefactor.race..wa.B
## edges 0.383598069
## nodefactor.deg.main.deg.pers.0.1 0.212363099
## nodefactor.deg.main.deg.pers.0.2 0.097873260
## nodefactor.deg.main.deg.pers.1.0 0.094079406
## nodefactor.deg.main.deg.pers.1.1 0.156273991
## nodefactor.deg.main.deg.pers.1.2 0.165164767
## nodefactor.race..wa.B 1.000000000
## nodefactor.race..wa.H 0.138560203
## nodefactor.region.EW 0.103077829
## nodefactor.region.OW 0.222142230
## nodematch.race..wa.B 0.353600441
## nodematch.race..wa.H -0.008394445
## nodematch.race..wa.O -0.005389756
## absdiff.sqrt.age 0.285350770
## nodefactor.race..wa.H
## edges 0.508375735
## nodefactor.deg.main.deg.pers.0.1 0.264292448
## nodefactor.deg.main.deg.pers.0.2 0.128752332
## nodefactor.deg.main.deg.pers.1.0 0.153375068
## nodefactor.deg.main.deg.pers.1.1 0.265349120
## nodefactor.deg.main.deg.pers.1.2 0.307801669
## nodefactor.race..wa.B 0.138560203
## nodefactor.race..wa.H 1.000000000
## nodefactor.region.EW 0.325830941
## nodefactor.region.OW 0.303640531
## nodematch.race..wa.B 0.003708876
## nodematch.race..wa.H 0.545236042
## nodematch.race..wa.O -0.012380987
## absdiff.sqrt.age 0.383963280
## nodefactor.region.EW nodefactor.region.OW
## edges 0.39823200 0.62722088
## nodefactor.deg.main.deg.pers.0.1 0.22256580 0.36280130
## nodefactor.deg.main.deg.pers.0.2 0.10002281 0.17357545
## nodefactor.deg.main.deg.pers.1.0 0.10632333 0.15997076
## nodefactor.deg.main.deg.pers.1.1 0.19387240 0.27184236
## nodefactor.deg.main.deg.pers.1.2 0.21869388 0.28353425
## nodefactor.race..wa.B 0.10307783 0.22214223
## nodefactor.race..wa.H 0.32583094 0.30364053
## nodefactor.region.EW 1.00000000 0.12004905
## nodefactor.region.OW 0.12004905 1.00000000
## nodematch.race..wa.B 0.01861416 0.04593225
## nodematch.race..wa.H 0.14152435 0.08737416
## nodematch.race..wa.O 0.25569434 0.49921057
## absdiff.sqrt.age 0.29859331 0.48644789
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.074509756 0.154320467
## nodefactor.deg.main.deg.pers.0.1 0.043041618 0.070879348
## nodefactor.deg.main.deg.pers.0.2 0.017284421 0.029370762
## nodefactor.deg.main.deg.pers.1.0 0.015329911 0.059175657
## nodefactor.deg.main.deg.pers.1.1 0.021739675 0.091689095
## nodefactor.deg.main.deg.pers.1.2 0.023712091 0.105083519
## nodefactor.race..wa.B 0.353600441 -0.008394445
## nodefactor.race..wa.H 0.003708876 0.545236042
## nodefactor.region.EW 0.018614162 0.141524351
## nodefactor.region.OW 0.045932254 0.087374160
## nodematch.race..wa.B 1.000000000 0.004709589
## nodematch.race..wa.H 0.004709589 1.000000000
## nodematch.race..wa.O 0.001568777 -0.009186186
## absdiff.sqrt.age 0.058868379 0.112325276
## nodematch.race..wa.O absdiff.sqrt.age
## edges 0.769004480 0.76230908
## nodefactor.deg.main.deg.pers.0.1 0.428692864 0.42057694
## nodefactor.deg.main.deg.pers.0.2 0.212423855 0.20957172
## nodefactor.deg.main.deg.pers.1.0 0.211094282 0.20802986
## nodefactor.deg.main.deg.pers.1.1 0.377778304 0.37003475
## nodefactor.deg.main.deg.pers.1.2 0.363537715 0.38453726
## nodefactor.race..wa.B -0.005389756 0.28535077
## nodefactor.race..wa.H -0.012380987 0.38396328
## nodefactor.region.EW 0.255694338 0.29859331
## nodefactor.region.OW 0.499210566 0.48644789
## nodematch.race..wa.B 0.001568777 0.05886838
## nodematch.race..wa.H -0.009186186 0.11232528
## nodematch.race..wa.O 1.000000000 0.59049730
## absdiff.sqrt.age 0.590497303 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 -0.01838498 -0.014758308
## Lag 2e+05 0.01598074 -0.001516905
## Lag 3e+05 -0.01026604 -0.013136896
## Lag 4e+05 -0.02123864 0.007924120
## Lag 5e+05 -0.01050437 0.019076229
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.010866929
## Lag 2e+05 0.005635868
## Lag 3e+05 -0.015559389
## Lag 4e+05 0.012906926
## Lag 5e+05 -0.023281395
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.015542960
## Lag 2e+05 0.008423469
## Lag 3e+05 0.009015947
## Lag 4e+05 0.001345310
## Lag 5e+05 0.028563287
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.029656545
## Lag 2e+05 0.009392623
## Lag 3e+05 -0.001069393
## Lag 4e+05 -0.026948922
## Lag 5e+05 -0.027750021
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.012147145 -0.005114458
## Lag 2e+05 -0.004677860 -0.023306932
## Lag 3e+05 -0.004397924 0.006063398
## Lag 4e+05 -0.004726936 0.001586781
## Lag 5e+05 0.004323503 0.026075273
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.012315141 -0.001891927 -0.002783458
## Lag 2e+05 0.009962233 -0.030583596 0.018226933
## Lag 3e+05 0.005961769 -0.023948307 -0.003996027
## Lag 4e+05 -0.001810003 0.010053793 -0.040188301
## Lag 5e+05 0.020095619 -0.015380388 -0.027218348
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0056056102 -0.009078095 -0.023409982
## Lag 2e+05 0.0007188766 -0.003902905 0.017190323
## Lag 3e+05 0.0199507505 0.021899713 0.002128706
## Lag 4e+05 -0.0070741928 -0.024633146 -0.024181541
## Lag 5e+05 -0.0102727114 -0.017095102 -0.023880260
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.044119152
## Lag 2e+05 0.010586364
## Lag 3e+05 -0.003137276
## Lag 4e+05 -0.043296112
## Lag 5e+05 0.001389253
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.002797316 0.038988894
## Lag 2e+05 -0.038066245 -0.017405002
## Lag 3e+05 0.047589817 0.002488176
## Lag 4e+05 0.017429996 -0.003775788
## Lag 5e+05 -0.012262448 0.013459546
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.004983675
## Lag 2e+05 -0.030674977
## Lag 3e+05 0.029695282
## Lag 4e+05 -0.010926361
## Lag 5e+05 0.005956721
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.034852175
## Lag 2e+05 0.021842281
## Lag 3e+05 -0.030134654
## Lag 4e+05 0.004468475
## Lag 5e+05 -0.045046513
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.020530197
## Lag 2e+05 -0.027099690
## Lag 3e+05 0.028883278
## Lag 4e+05 0.003580288
## Lag 5e+05 -0.008334724
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.013103530 0.0121560155
## Lag 2e+05 0.001431149 0.0132639289
## Lag 3e+05 0.035470918 0.0121829557
## Lag 4e+05 0.024992887 0.0003554095
## Lag 5e+05 -0.002261401 -0.0118712153
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.015284727 -0.027435351 0.022614818
## Lag 2e+05 -0.020994257 -0.013873569 -0.016894617
## Lag 3e+05 0.007215828 0.039568373 0.008872646
## Lag 4e+05 0.006690702 0.028559402 -0.003144509
## Lag 5e+05 -0.014126937 -0.002518722 -0.003657729
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.007407616 0.06438844 0.001810974
## Lag 2e+05 -0.002848102 -0.01988282 -0.049205293
## Lag 3e+05 -0.010013317 0.03849992 0.021323101
## Lag 4e+05 0.013148068 0.01176687 0.003271264
## Lag 5e+05 -0.023046487 -0.01553140 -0.004767803
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.002175808
## Lag 2e+05 -0.028038084
## Lag 3e+05 0.030437855
## Lag 4e+05 0.001604587
## Lag 5e+05 0.015139470
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.016221742 0.0071324881
## Lag 2e+05 -0.006747327 0.0190595001
## Lag 3e+05 0.010805860 -0.0005895686
## Lag 4e+05 -0.036288916 -0.0137712801
## Lag 5e+05 -0.001143926 0.0117173882
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.007669356
## Lag 2e+05 -0.013881988
## Lag 3e+05 -0.022510622
## Lag 4e+05 0.018340721
## Lag 5e+05 -0.026609009
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.016607362
## Lag 2e+05 0.009811064
## Lag 3e+05 0.005721957
## Lag 4e+05 -0.018348484
## Lag 5e+05 -0.018174479
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.016967372
## Lag 2e+05 -0.007939954
## Lag 3e+05 0.022392378
## Lag 4e+05 -0.021146710
## Lag 5e+05 -0.023491414
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.001253585 -0.0204970162
## Lag 2e+05 -0.014765554 0.0009863994
## Lag 3e+05 -0.023103602 -0.0049348678
## Lag 4e+05 -0.002262182 -0.0042127896
## Lag 5e+05 -0.040906716 -0.0080274482
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0187468910 -0.006204450 0.014792826
## Lag 2e+05 0.0318280919 0.012656945 -0.006674452
## Lag 3e+05 -0.0035541951 0.026423320 -0.024725030
## Lag 4e+05 -0.0256128812 -0.019748876 -0.034559350
## Lag 5e+05 -0.0004273554 -0.008824892 -0.010108314
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 -0.017573194 -0.01971700 0.006406518
## Lag 2e+05 -0.004895722 0.03893733 -0.015182336
## Lag 3e+05 -0.010218188 0.01169082 0.014071629
## Lag 4e+05 -0.014692188 -0.02141986 -0.037055358
## Lag 5e+05 0.008893737 -0.01138526 -0.021145884
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.013904538
## Lag 2e+05 0.004831224
## Lag 3e+05 0.011585412
## Lag 4e+05 -0.029583173
## Lag 5e+05 -0.011014603
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.02944270 0.039076159
## Lag 2e+05 0.01237700 -0.007652163
## Lag 3e+05 0.01298179 -0.010532059
## Lag 4e+05 -0.02835684 -0.026995775
## Lag 5e+05 0.01490372 -0.001968773
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.032256470
## Lag 2e+05 0.025961615
## Lag 3e+05 0.028824602
## Lag 4e+05 -0.016019001
## Lag 5e+05 -0.008991554
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.002384740
## Lag 2e+05 0.012333401
## Lag 3e+05 -0.004728158
## Lag 4e+05 0.001012376
## Lag 5e+05 0.010337732
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000000
## Lag 1e+05 0.0002776783
## Lag 2e+05 0.0041557116
## Lag 3e+05 -0.0023088154
## Lag 4e+05 -0.0125740408
## Lag 5e+05 0.0202105115
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.00000000
## Lag 1e+05 0.0180114675 0.01300494
## Lag 2e+05 -0.0264834231 0.01367811
## Lag 3e+05 -0.0068646337 -0.02203863
## Lag 4e+05 0.0008971107 -0.02793439
## Lag 5e+05 0.0176539419 -0.01346020
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.019669866 0.0015491994 0.002777396
## Lag 2e+05 0.018143194 -0.0106291663 0.018364519
## Lag 3e+05 0.002698024 0.0100622655 -0.007571935
## Lag 4e+05 -0.004657459 -0.0071402950 -0.003697450
## Lag 5e+05 0.014956455 0.0004633712 0.003875354
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.018422227 0.006099997 0.009584843
## Lag 2e+05 -0.020916839 0.017683053 -0.004631584
## Lag 3e+05 -0.003998694 -0.015934664 0.017198737
## Lag 4e+05 0.032630478 0.005948582 -0.012980987
## Lag 5e+05 -0.013172548 0.004707689 0.008274248
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.028319897
## Lag 2e+05 0.002121496
## Lag 3e+05 0.003934470
## Lag 4e+05 -0.039136350
## Lag 5e+05 0.007275778
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.007085999 0.002911065
## Lag 2e+05 -0.001770386 -0.002444332
## Lag 3e+05 0.011976435 -0.018246576
## Lag 4e+05 -0.014880791 0.026971282
## Lag 5e+05 0.008472621 0.008524512
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.006265977
## Lag 2e+05 -0.020632008
## Lag 3e+05 0.013111568
## Lag 4e+05 0.006837112
## Lag 5e+05 0.005094957
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.008486743
## Lag 2e+05 0.018258017
## Lag 3e+05 0.008143710
## Lag 4e+05 0.006046122
## Lag 5e+05 0.012240606
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.004383539
## Lag 2e+05 0.047310856
## Lag 3e+05 -0.001373634
## Lag 4e+05 0.001277371
## Lag 5e+05 -0.003594589
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.021624539 0.027523681
## Lag 2e+05 -0.013465653 -0.004758868
## Lag 3e+05 0.007313888 -0.003224983
## Lag 4e+05 0.021748918 -0.007972807
## Lag 5e+05 -0.012295394 0.006879417
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.026610716 -0.019961770 -0.005881322
## Lag 2e+05 -0.003810682 0.050858615 0.024571134
## Lag 3e+05 0.022186457 -0.002702699 0.007690097
## Lag 4e+05 -0.007414182 0.010086678 -0.012001326
## Lag 5e+05 0.009428566 -0.017933716 0.001602909
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.017439889 0.004833652 -0.0163441685
## Lag 2e+05 0.030550952 -0.011510787 -0.0111230792
## Lag 3e+05 -0.011202169 0.001261522 0.0001902394
## Lag 4e+05 -0.001939769 0.015829435 0.0105400730
## Lag 5e+05 -0.011598238 -0.004461001 0.0056487008
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.009031477
## Lag 2e+05 -0.008959441
## Lag 3e+05 -0.022692882
## Lag 4e+05 -0.029572325
## Lag 5e+05 0.036854862
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.006538330 -0.014553577
## Lag 2e+05 0.007679586 0.023373584
## Lag 3e+05 0.001931852 -0.006439550
## Lag 4e+05 0.017374787 -0.007203221
## Lag 5e+05 -0.001841826 -0.027269260
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.009915449
## Lag 2e+05 -0.005752965
## Lag 3e+05 -0.014988973
## Lag 4e+05 -0.005819164
## Lag 5e+05 0.010308823
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.006679984
## Lag 2e+05 0.018128687
## Lag 3e+05 -0.013440242
## Lag 4e+05 0.014864376
## Lag 5e+05 -0.021507809
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.038644902
## Lag 2e+05 0.024109570
## Lag 3e+05 -0.005750718
## Lag 4e+05 -0.002202024
## Lag 5e+05 -0.007177422
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.0000000000
## Lag 1e+05 -0.0359055375 -0.0039847583
## Lag 2e+05 -0.0073360971 0.0012836860
## Lag 3e+05 -0.0095993528 0.0002155454
## Lag 4e+05 -0.0122061165 -0.0072916694
## Lag 5e+05 -0.0009764361 0.0259174612
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.033182401 0.0275211992 0.020074815
## Lag 2e+05 -0.012549649 -0.0018563742 0.006354528
## Lag 3e+05 -0.006085256 0.0027181001 -0.017308858
## Lag 4e+05 0.002800106 0.0186912577 0.001022046
## Lag 5e+05 0.005229004 -0.0009137091 -0.009309480
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.010104764 0.002196446 0.017252674
## Lag 2e+05 0.007319048 -0.014213306 0.007326767
## Lag 3e+05 -0.004725972 0.008523261 0.009172682
## Lag 4e+05 0.015811489 -0.011237865 0.003531728
## Lag 5e+05 -0.009337976 -0.005538742 -0.003536018
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.017539938
## Lag 2e+05 -0.004069927
## Lag 3e+05 -0.012545034
## Lag 4e+05 -0.001138002
## Lag 5e+05 0.011306997
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 -0.0009342177 -0.003295265
## Lag 2e+05 0.0127426469 0.040990789
## Lag 3e+05 0.0269902732 0.012936585
## Lag 4e+05 0.0127684808 0.007218824
## Lag 5e+05 -0.0091356638 -0.032971143
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 -0.0054659436
## Lag 2e+05 0.0062205825
## Lag 3e+05 0.0128192101
## Lag 4e+05 0.0057945877
## Lag 5e+05 0.0006908509
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.021432275
## Lag 2e+05 0.004167360
## Lag 3e+05 -0.010601356
## Lag 4e+05 -0.000208179
## Lag 5e+05 -0.046646246
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.008611236
## Lag 2e+05 -0.004873903
## Lag 3e+05 -0.001276032
## Lag 4e+05 0.009541293
## Lag 5e+05 -0.015682131
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.010416599 0.0337659123
## Lag 2e+05 0.008254769 -0.0075931293
## Lag 3e+05 0.017394393 -0.0001458916
## Lag 4e+05 -0.022338268 -0.0038586366
## Lag 5e+05 -0.008548088 -0.0205013780
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0007526209 -0.004035808 0.000622854
## Lag 2e+05 0.0064666518 0.014809456 -0.007288903
## Lag 3e+05 -0.0132908228 0.003708138 -0.033447724
## Lag 4e+05 0.0306068105 0.011943353 0.003934845
## Lag 5e+05 0.0218326702 -0.007536426 -0.012805164
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.001878144 0.008468449 -0.0093726387
## Lag 2e+05 0.009550675 0.001333992 0.0006659832
## Lag 3e+05 0.010640822 -0.036951358 0.0213123517
## Lag 4e+05 0.010491045 -0.006448011 0.0028458846
## Lag 5e+05 -0.002138641 -0.004233080 0.0023277856
## absdiff.sqrt.age
## Lag 0 1.00000000
## Lag 1e+05 -0.01236563
## Lag 2e+05 0.01647964
## Lag 3e+05 0.01464942
## Lag 4e+05 0.03041538
## Lag 5e+05 -0.01139389
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 -0.0017686449 0.008063900
## Lag 2e+05 0.0403405356 0.007485370
## Lag 3e+05 0.0005160806 -0.013689582
## Lag 4e+05 0.0082160134 0.014611285
## Lag 5e+05 -0.0066789900 -0.001524109
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.016555001
## Lag 2e+05 0.013099205
## Lag 3e+05 -0.026657237
## Lag 4e+05 0.038205957
## Lag 5e+05 0.001330595
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.030292045
## Lag 2e+05 0.009607327
## Lag 3e+05 -0.008053026
## Lag 4e+05 0.059388537
## Lag 5e+05 -0.024092744
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000000
## Lag 1e+05 0.0026146777
## Lag 2e+05 -0.0073107938
## Lag 3e+05 -0.0040333711
## Lag 4e+05 -0.0104992134
## Lag 5e+05 -0.0008759704
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.018565085 0.018864567
## Lag 2e+05 0.032630889 -0.028497744
## Lag 3e+05 0.001595481 0.011000142
## Lag 4e+05 0.014183109 -0.023865864
## Lag 5e+05 0.030730078 0.005545485
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.0133581672 -0.030107073 0.0082376103
## Lag 2e+05 0.0006492938 -0.009654135 0.0387046383
## Lag 3e+05 0.0044340161 -0.001866520 -0.0009748118
## Lag 4e+05 -0.0117762565 -0.004907252 -0.0093293641
## Lag 5e+05 -0.0006555530 -0.015473440 -0.0127421300
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.000000000 1.00000000
## Lag 1e+05 -0.0051308174 0.009047269 0.01718919
## Lag 2e+05 -0.0188945833 0.009964113 0.02564848
## Lag 3e+05 0.0030721403 0.010128556 0.01682155
## Lag 4e+05 0.0006633494 -0.021604822 0.01357745
## Lag 5e+05 0.0092564064 0.007676774 -0.01811159
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.026404174
## Lag 2e+05 0.013427761
## Lag 3e+05 0.003649105
## Lag 4e+05 0.007222914
## Lag 5e+05 0.008666724
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.5775 -0.5633
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.1446 0.5731
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.6971 -1.5456
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.5895 0.5404
## nodefactor.region.EW nodefactor.region.OW
## -1.8048 -0.4440
## nodematch.race..wa.B nodematch.race..wa.H
## -1.2349 1.0565
## nodematch.race..wa.O absdiff.sqrt.age
## -0.4700 -0.2203
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.56362106 0.57322397
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.88499260 0.56654569
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.48576691 0.12219312
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.55550642 0.58890510
## nodefactor.region.EW nodefactor.region.OW
## 0.07111079 0.65702886
## nodematch.race..wa.B nodematch.race..wa.H
## 0.21688403 0.29072186
## nodematch.race..wa.O absdiff.sqrt.age
## 0.63833332 0.82565892
## Joint P-value (lower = worse): 0.9642196 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.65022 1.12436
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.27883 0.04935
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.77984 0.11528
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.84518 -0.44186
## nodefactor.region.EW nodefactor.region.OW
## 0.76834 1.15071
## nodematch.race..wa.B nodematch.race..wa.H
## 0.25280 -1.02093
## nodematch.race..wa.O absdiff.sqrt.age
## 2.07193 0.73713
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.09889794 0.26086195
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.20095748 0.96064147
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.07510265 0.90821925
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.39801067 0.65859143
## nodefactor.region.EW nodefactor.region.OW
## 0.44228531 0.24985207
## nodematch.race..wa.B nodematch.race..wa.H
## 0.80042316 0.30728600
## nodematch.race..wa.O absdiff.sqrt.age
## 0.03827173 0.46104217
## Joint P-value (lower = worse): 0.9485581 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.24659 -1.63185
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.52043 -0.22741
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -1.87911 0.37078
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.92635 -1.12713
## nodefactor.region.EW nodefactor.region.OW
## 0.01893 -0.97085
## nodematch.race..wa.B nodematch.race..wa.H
## -1.52098 -1.10867
## nodematch.race..wa.O absdiff.sqrt.age
## -0.38045 -1.43691
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.21254786 0.10271116
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.60276484 0.82010193
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.06022907 0.71079813
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.35426583 0.25968835
## nodefactor.region.EW nodefactor.region.OW
## 0.98489550 0.33162201
## nodematch.race..wa.B nodematch.race..wa.H
## 0.12826389 0.26757239
## nodematch.race..wa.O absdiff.sqrt.age
## 0.70361051 0.15074343
## Joint P-value (lower = worse): 0.9631895 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.4686 -1.5053
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.7964 1.0538
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.9165 -1.1164
## nodefactor.race..wa.B nodefactor.race..wa.H
## 1.2988 -1.1781
## nodefactor.region.EW nodefactor.region.OW
## 0.6065 0.2100
## nodematch.race..wa.B nodematch.race..wa.H
## 0.9606 0.3427
## nodematch.race..wa.O absdiff.sqrt.age
## 0.1154 0.4221
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.6393582 0.1322577
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.4257909 0.2919848
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.3594114 0.2642340
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1940026 0.2387440
## nodefactor.region.EW nodefactor.region.OW
## 0.5441590 0.8336784
## nodematch.race..wa.B nodematch.race..wa.H
## 0.3367365 0.7318272
## nodematch.race..wa.O absdiff.sqrt.age
## 0.9081239 0.6729393
## Joint P-value (lower = worse): 0.7606975 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.4640 1.7062
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.7765 0.4734
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.7141 -0.1294
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7970 0.3404
## nodefactor.region.EW nodefactor.region.OW
## 1.4964 0.9669
## nodematch.race..wa.B nodematch.race..wa.H
## 0.3484 -0.3286
## nodematch.race..wa.O absdiff.sqrt.age
## 0.2490 0.4076
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.64261530 0.08796631
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.43743215 0.63589976
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.47514108 0.89700287
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.42547410 0.73359247
## nodefactor.region.EW nodefactor.region.OW
## 0.13455832 0.33360477
## nodematch.race..wa.B nodematch.race..wa.H
## 0.72754198 0.74244474
## nodematch.race..wa.O absdiff.sqrt.age
## 0.80334916 0.68355687
## Joint P-value (lower = worse): 0.9233332 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.23728 -0.72416
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 2.18659 0.01911
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -1.18780 1.45040
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.88590 0.72793
## nodefactor.region.EW nodefactor.region.OW
## 1.09384 1.34181
## nodematch.race..wa.B nodematch.race..wa.H
## -2.08408 0.33465
## nodematch.race..wa.O absdiff.sqrt.age
## -0.19692 1.45641
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.81243636 0.46896704
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.02877223 0.98475651
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.23491297 0.14694680
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.37566974 0.46665942
## nodefactor.region.EW nodefactor.region.OW
## 0.27402593 0.17965873
## nodematch.race..wa.B nodematch.race..wa.H
## 0.03715314 0.73789224
## nodematch.race..wa.O absdiff.sqrt.age
## 0.84389355 0.14527989
## Joint P-value (lower = worse): 0.4932672 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.27993 -0.73026
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.79142 -0.33090
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.27443 1.12842
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.79965 0.91546
## nodefactor.region.EW nodefactor.region.OW
## 0.26616 0.07421
## nodematch.race..wa.B nodematch.race..wa.H
## 1.47345 -1.07903
## nodematch.race..wa.O absdiff.sqrt.age
## -0.90519 -0.49806
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.7795318 0.4652306
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.4286985 0.7407165
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.7837510 0.2591416
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4239143 0.3599514
## nodefactor.region.EW nodefactor.region.OW
## 0.7901131 0.9408405
## nodematch.race..wa.B nodematch.race..wa.H
## 0.1406287 0.2805728
## nodematch.race..wa.O absdiff.sqrt.age
## 0.3653633 0.6184422
## Joint P-value (lower = worse): 0.7142981 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.56787 -0.15500
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.09708 1.93580
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.15349 0.01116
## nodefactor.race..wa.B nodefactor.race..wa.H
## 1.33038 0.40114
## nodefactor.region.EW nodefactor.region.OW
## 0.40107 0.72834
## nodematch.race..wa.B nodematch.race..wa.H
## 1.16342 1.04032
## nodematch.race..wa.O absdiff.sqrt.age
## 1.16872 2.02341
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.11691222 0.87682385
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.27260850 0.05289245
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.87801030 0.99109208
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.18339393 0.68831443
## nodefactor.region.EW nodefactor.region.OW
## 0.68836743 0.46640404
## nodematch.race..wa.B nodematch.race..wa.H
## 0.24466104 0.29819193
## nodematch.race..wa.O absdiff.sqrt.age
## 0.24251636 0.04303100
## Joint P-value (lower = worse): 0.6608029 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 7
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.0136128 21.782 0.125760 0.354999
## nodefactor.deg.main.deg.pers.0.1 -0.0190067 14.279 0.082438 0.280311
## nodefactor.deg.main.deg.pers.0.2 -0.0525007 6.131 0.035400 0.061477
## nodefactor.deg.main.deg.pers.1.0 0.0034290 6.238 0.036013 0.041345
## nodefactor.deg.main.deg.pers.1.1 0.5429360 12.594 0.072713 0.256609
## nodefactor.deg.main.deg.pers.1.2 -0.3842240 12.755 0.073640 0.268522
## nodefactor.riskg.O2 -0.4009250 0.000 0.000000 0.000000
## nodefactor.riskg.O3 -0.4009250 0.000 0.000000 0.000000
## nodefactor.riskg.O4 0.0013825 2.608 0.015059 0.015084
## nodefactor.riskg.Y1 -0.1745304 10.810 0.062411 0.071594
## nodefactor.riskg.Y2 0.0152250 1.170 0.006754 0.006692
## nodefactor.riskg.Y3 -0.0435427 2.861 0.016518 0.016520
## nodefactor.riskg.Y4 0.0055347 8.629 0.049817 0.052549
## nodefactor.race..wa.B 0.1290173 8.967 0.051774 0.157597
## nodefactor.race..wa.H -0.1319200 13.218 0.076315 0.278496
## nodefactor.region.EW 0.4876244 9.496 0.054827 0.152817
## nodefactor.region.OW -0.8185392 17.207 0.099344 0.229113
## nodematch.race..wa.B 0.0005486 1.589 0.009177 0.026388
## nodematch.race..wa.H 0.0706131 3.659 0.021125 0.093845
## nodematch.race..wa.O 0.2803769 16.859 0.097335 0.257541
## absdiff.sqrt.age -0.1657123 26.564 0.153367 0.191959
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75%
## edges -42.1586 -15.1586 -0.15862 14.8414
## nodefactor.deg.main.deg.pers.0.1 -27.3100 -9.3100 -0.31004 9.6900
## nodefactor.deg.main.deg.pers.0.2 -11.3710 -4.3710 -0.37103 3.6290
## nodefactor.deg.main.deg.pers.1.0 -12.0335 -4.0335 -0.03347 3.9665
## nodefactor.deg.main.deg.pers.1.1 -23.5379 -8.5379 0.46214 8.4621
## nodefactor.deg.main.deg.pers.1.2 -24.3881 -9.3881 -0.38812 7.6119
## nodefactor.riskg.O2 -0.4009 -0.4009 -0.40092 -0.4009
## nodefactor.riskg.O3 -0.4009 -0.4009 -0.40092 -0.4009
## nodefactor.riskg.O4 -4.8558 -1.8558 0.14418 1.3942
## nodefactor.riskg.Y1 -20.5127 -7.5127 -0.51266 7.4873
## nodefactor.riskg.Y2 -1.3491 -1.3491 -0.34908 0.6509
## nodefactor.riskg.Y3 -5.2024 -2.2024 -0.20238 1.7976
## nodefactor.riskg.Y4 -15.7860 -5.7860 0.21403 5.2140
## nodefactor.race..wa.B -16.5908 -5.5908 0.40918 6.4092
## nodefactor.race..wa.H -25.1739 -9.1739 -0.17392 8.8261
## nodefactor.region.EW -17.5014 -6.5014 0.49862 6.4986
## nodefactor.region.OW -33.4862 -12.4862 -1.48621 10.5138
## nodematch.race..wa.B -2.5399 -1.5399 -0.53985 1.4601
## nodematch.race..wa.H -6.2690 -2.2690 -0.26902 2.7310
## nodematch.race..wa.O -31.8800 -10.8800 0.11998 11.1200
## absdiff.sqrt.age -50.4986 -18.3496 -0.70761 17.3135
## 97.5%
## edges 42.8414
## nodefactor.deg.main.deg.pers.0.1 28.6900
## nodefactor.deg.main.deg.pers.0.2 12.6290
## nodefactor.deg.main.deg.pers.1.0 12.9665
## nodefactor.deg.main.deg.pers.1.1 25.4621
## nodefactor.deg.main.deg.pers.1.2 25.6119
## nodefactor.riskg.O2 -0.4009
## nodefactor.riskg.O3 -0.4009
## nodefactor.riskg.O4 5.1442
## nodefactor.riskg.Y1 21.4873
## nodefactor.riskg.Y2 2.6509
## nodefactor.riskg.Y3 5.7976
## nodefactor.riskg.Y4 17.2140
## nodefactor.race..wa.B 18.4092
## nodefactor.race..wa.H 26.8261
## nodefactor.region.EW 19.4986
## nodefactor.region.OW 33.5138
## nodematch.race..wa.B 3.4601
## nodematch.race..wa.H 7.7310
## nodematch.race..wa.O 34.1200
## absdiff.sqrt.age 53.5492
##
##
## Sample statistics cross-correlations:## Warning in cor(as.matrix(x)): the standard deviation is zero## edges
## edges 1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.55269216
## nodefactor.deg.main.deg.pers.0.2 0.26174052
## nodefactor.deg.main.deg.pers.1.0 0.27712515
## nodefactor.deg.main.deg.pers.1.1 0.50354638
## nodefactor.deg.main.deg.pers.1.2 0.50771115
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 NA
## nodefactor.riskg.O4 0.12806573
## nodefactor.riskg.Y1 0.46715572
## nodefactor.riskg.Y2 0.04654453
## nodefactor.riskg.Y3 0.13566357
## nodefactor.riskg.Y4 0.37410624
## nodefactor.race..wa.B 0.38977825
## nodefactor.race..wa.H 0.50879071
## nodefactor.region.EW 0.40447032
## nodefactor.region.OW 0.62947557
## nodematch.race..wa.B 0.07365090
## nodematch.race..wa.H 0.16121496
## nodematch.race..wa.O 0.77114338
## absdiff.sqrt.age 0.66328459
## nodefactor.deg.main.deg.pers.0.1
## edges 0.55269216
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.07849312
## nodefactor.deg.main.deg.pers.1.0 0.08195463
## nodefactor.deg.main.deg.pers.1.1 0.14089799
## nodefactor.deg.main.deg.pers.1.2 0.13908267
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 NA
## nodefactor.riskg.O4 0.08383399
## nodefactor.riskg.Y1 0.28374261
## nodefactor.riskg.Y2 0.02038057
## nodefactor.riskg.Y3 0.07969436
## nodefactor.riskg.Y4 0.19842005
## nodefactor.race..wa.B 0.15419755
## nodefactor.race..wa.H 0.22210543
## nodefactor.region.EW 0.23884984
## nodefactor.region.OW 0.37347896
## nodematch.race..wa.B 0.02279795
## nodematch.race..wa.H 0.04987994
## nodematch.race..wa.O 0.48518349
## absdiff.sqrt.age 0.37992711
## nodefactor.deg.main.deg.pers.0.2
## edges 0.26174052
## nodefactor.deg.main.deg.pers.0.1 0.07849312
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.03756479
## nodefactor.deg.main.deg.pers.1.1 0.05529445
## nodefactor.deg.main.deg.pers.1.2 0.06309319
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 NA
## nodefactor.riskg.O4 0.02264006
## nodefactor.riskg.Y1 0.11547677
## nodefactor.riskg.Y2 0.01972302
## nodefactor.riskg.Y3 0.02992664
## nodefactor.riskg.Y4 0.08371044
## nodefactor.race..wa.B 0.12981358
## nodefactor.race..wa.H 0.14518061
## nodefactor.region.EW 0.11146497
## nodefactor.region.OW 0.17611740
## nodematch.race..wa.B 0.01655142
## nodematch.race..wa.H 0.05679196
## nodematch.race..wa.O 0.18806812
## absdiff.sqrt.age 0.16704498
## nodefactor.deg.main.deg.pers.1.0
## edges 0.277125145
## nodefactor.deg.main.deg.pers.0.1 0.081954633
## nodefactor.deg.main.deg.pers.0.2 0.037564790
## nodefactor.deg.main.deg.pers.1.0 1.000000000
## nodefactor.deg.main.deg.pers.1.1 0.060623339
## nodefactor.deg.main.deg.pers.1.2 0.075645011
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 NA
## nodefactor.riskg.O4 0.035389247
## nodefactor.riskg.Y1 0.122496625
## nodefactor.riskg.Y2 0.009880225
## nodefactor.riskg.Y3 0.035805591
## nodefactor.riskg.Y4 0.091321357
## nodefactor.race..wa.B 0.117436055
## nodefactor.race..wa.H 0.162080744
## nodefactor.region.EW 0.101109682
## nodefactor.region.OW 0.158470687
## nodematch.race..wa.B 0.018760530
## nodematch.race..wa.H 0.055354186
## nodematch.race..wa.O 0.199257990
## absdiff.sqrt.age 0.180203661
## nodefactor.deg.main.deg.pers.1.1
## edges 0.50354638
## nodefactor.deg.main.deg.pers.0.1 0.14089799
## nodefactor.deg.main.deg.pers.0.2 0.05529445
## nodefactor.deg.main.deg.pers.1.0 0.06062334
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.12943163
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 NA
## nodefactor.riskg.O4 0.05119761
## nodefactor.riskg.Y1 0.21513790
## nodefactor.riskg.Y2 0.02038598
## nodefactor.riskg.Y3 0.05548881
## nodefactor.riskg.Y4 0.18785359
## nodefactor.race..wa.B 0.17186241
## nodefactor.race..wa.H 0.26729957
## nodefactor.region.EW 0.21920779
## nodefactor.region.OW 0.32524044
## nodematch.race..wa.B 0.02500340
## nodematch.race..wa.H 0.08729138
## nodematch.race..wa.O 0.38746593
## absdiff.sqrt.age 0.32642527
## nodefactor.deg.main.deg.pers.1.2
## edges 0.50771115
## nodefactor.deg.main.deg.pers.0.1 0.13908267
## nodefactor.deg.main.deg.pers.0.2 0.06309319
## nodefactor.deg.main.deg.pers.1.0 0.07564501
## nodefactor.deg.main.deg.pers.1.1 0.12943163
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 NA
## nodefactor.riskg.O4 0.05995723
## nodefactor.riskg.Y1 0.25634694
## nodefactor.riskg.Y2 0.02976379
## nodefactor.riskg.Y3 0.08078148
## nodefactor.riskg.Y4 0.21808686
## nodefactor.race..wa.B 0.17652196
## nodefactor.race..wa.H 0.33790579
## nodefactor.region.EW 0.21285425
## nodefactor.region.OW 0.29408965
## nodematch.race..wa.B 0.01939028
## nodematch.race..wa.H 0.12578955
## nodematch.race..wa.O 0.35282011
## absdiff.sqrt.age 0.34824833
## nodefactor.riskg.O2 nodefactor.riskg.O3
## edges NA NA
## nodefactor.deg.main.deg.pers.0.1 NA NA
## nodefactor.deg.main.deg.pers.0.2 NA NA
## nodefactor.deg.main.deg.pers.1.0 NA NA
## nodefactor.deg.main.deg.pers.1.1 NA NA
## nodefactor.deg.main.deg.pers.1.2 NA NA
## nodefactor.riskg.O2 1 NA
## nodefactor.riskg.O3 NA 1
## nodefactor.riskg.O4 NA NA
## nodefactor.riskg.Y1 NA NA
## nodefactor.riskg.Y2 NA NA
## nodefactor.riskg.Y3 NA NA
## nodefactor.riskg.Y4 NA NA
## nodefactor.race..wa.B NA NA
## nodefactor.race..wa.H NA NA
## nodefactor.region.EW NA NA
## nodefactor.region.OW NA NA
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H NA NA
## nodematch.race..wa.O NA NA
## absdiff.sqrt.age NA NA
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## edges 0.128065728 0.46715572
## nodefactor.deg.main.deg.pers.0.1 0.083833992 0.28374261
## nodefactor.deg.main.deg.pers.0.2 0.022640056 0.11547677
## nodefactor.deg.main.deg.pers.1.0 0.035389247 0.12249662
## nodefactor.deg.main.deg.pers.1.1 0.051197615 0.21513790
## nodefactor.deg.main.deg.pers.1.2 0.059957233 0.25634694
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 NA NA
## nodefactor.riskg.O4 1.000000000 0.03871932
## nodefactor.riskg.Y1 0.038719324 1.00000000
## nodefactor.riskg.Y2 0.007873386 0.00266916
## nodefactor.riskg.Y3 0.012072310 0.02278592
## nodefactor.riskg.Y4 0.029387499 0.05418054
## nodefactor.race..wa.B 0.060141306 0.22540057
## nodefactor.race..wa.H 0.081299659 0.25828696
## nodefactor.region.EW 0.054632158 0.19378285
## nodefactor.region.OW 0.076259678 0.28958441
## nodematch.race..wa.B 0.007044216 0.05159728
## nodematch.race..wa.H 0.031002950 0.09477882
## nodematch.race..wa.O 0.085205807 0.33268661
## absdiff.sqrt.age 0.062991552 0.65415205
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## edges 0.0465445319 0.135663573
## nodefactor.deg.main.deg.pers.0.1 0.0203805723 0.079694365
## nodefactor.deg.main.deg.pers.0.2 0.0197230196 0.029926636
## nodefactor.deg.main.deg.pers.1.0 0.0098802251 0.035805591
## nodefactor.deg.main.deg.pers.1.1 0.0203859849 0.055488814
## nodefactor.deg.main.deg.pers.1.2 0.0297637900 0.080781475
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 NA NA
## nodefactor.riskg.O4 0.0078733859 0.012072310
## nodefactor.riskg.Y1 0.0026691604 0.022785916
## nodefactor.riskg.Y2 1.0000000000 0.009692505
## nodefactor.riskg.Y3 0.0096925049 1.000000000
## nodefactor.riskg.Y4 0.0005042811 0.020391005
## nodefactor.race..wa.B 0.0270960675 0.066451184
## nodefactor.race..wa.H 0.0257554212 0.070953113
## nodefactor.region.EW 0.0082346753 0.060586693
## nodefactor.region.OW 0.0310877707 0.089841809
## nodematch.race..wa.B 0.0087124436 0.024623216
## nodematch.race..wa.H 0.0093233873 0.023283323
## nodematch.race..wa.O 0.0310872600 0.099995382
## absdiff.sqrt.age 0.0699171598 0.186565331
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## edges 0.3741062379 0.389778246
## nodefactor.deg.main.deg.pers.0.1 0.1984200470 0.154197555
## nodefactor.deg.main.deg.pers.0.2 0.0837104389 0.129813581
## nodefactor.deg.main.deg.pers.1.0 0.0913213569 0.117436055
## nodefactor.deg.main.deg.pers.1.1 0.1878535851 0.171862408
## nodefactor.deg.main.deg.pers.1.2 0.2180868583 0.176521963
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 NA NA
## nodefactor.riskg.O4 0.0293874992 0.060141306
## nodefactor.riskg.Y1 0.0541805436 0.225400574
## nodefactor.riskg.Y2 0.0005042811 0.027096067
## nodefactor.riskg.Y3 0.0203910049 0.066451184
## nodefactor.riskg.Y4 1.0000000000 0.195347842
## nodefactor.race..wa.B 0.1953478415 1.000000000
## nodefactor.race..wa.H 0.2112030663 0.153251606
## nodefactor.region.EW 0.1729928808 0.100289520
## nodefactor.region.OW 0.2348012060 0.253972658
## nodematch.race..wa.B 0.0534265025 0.357320876
## nodematch.race..wa.H 0.0747140168 0.011459436
## nodematch.race..wa.O 0.2598511157 -0.001793241
## absdiff.sqrt.age 0.5367016981 0.312886441
## nodefactor.race..wa.H
## edges 0.508790705
## nodefactor.deg.main.deg.pers.0.1 0.222105430
## nodefactor.deg.main.deg.pers.0.2 0.145180608
## nodefactor.deg.main.deg.pers.1.0 0.162080744
## nodefactor.deg.main.deg.pers.1.1 0.267299575
## nodefactor.deg.main.deg.pers.1.2 0.337905788
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 NA
## nodefactor.riskg.O4 0.081299659
## nodefactor.riskg.Y1 0.258286957
## nodefactor.riskg.Y2 0.025755421
## nodefactor.riskg.Y3 0.070953113
## nodefactor.riskg.Y4 0.211203066
## nodefactor.race..wa.B 0.153251606
## nodefactor.race..wa.H 1.000000000
## nodefactor.region.EW 0.321712777
## nodefactor.region.OW 0.288660808
## nodematch.race..wa.B -0.001061294
## nodematch.race..wa.H 0.552081496
## nodematch.race..wa.O -0.011658978
## absdiff.sqrt.age 0.362909784
## nodefactor.region.EW nodefactor.region.OW
## edges 0.404470315 0.62947557
## nodefactor.deg.main.deg.pers.0.1 0.238849837 0.37347896
## nodefactor.deg.main.deg.pers.0.2 0.111464974 0.17611740
## nodefactor.deg.main.deg.pers.1.0 0.101109682 0.15847069
## nodefactor.deg.main.deg.pers.1.1 0.219207794 0.32524044
## nodefactor.deg.main.deg.pers.1.2 0.212854247 0.29408965
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 NA NA
## nodefactor.riskg.O4 0.054632158 0.07625968
## nodefactor.riskg.Y1 0.193782849 0.28958441
## nodefactor.riskg.Y2 0.008234675 0.03108777
## nodefactor.riskg.Y3 0.060586693 0.08984181
## nodefactor.riskg.Y4 0.172992881 0.23480121
## nodefactor.race..wa.B 0.100289520 0.25397266
## nodefactor.race..wa.H 0.321712777 0.28866081
## nodefactor.region.EW 1.000000000 0.12740576
## nodefactor.region.OW 0.127405759 1.00000000
## nodematch.race..wa.B -0.002267354 0.05774980
## nodematch.race..wa.H 0.144149364 0.08665417
## nodematch.race..wa.O 0.266012263 0.49958119
## absdiff.sqrt.age 0.272355906 0.41679033
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.073650899 0.161214962
## nodefactor.deg.main.deg.pers.0.1 0.022797950 0.049879941
## nodefactor.deg.main.deg.pers.0.2 0.016551425 0.056791962
## nodefactor.deg.main.deg.pers.1.0 0.018760530 0.055354186
## nodefactor.deg.main.deg.pers.1.1 0.025003396 0.087291377
## nodefactor.deg.main.deg.pers.1.2 0.019390282 0.125789552
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 NA NA
## nodefactor.riskg.O4 0.007044216 0.031002950
## nodefactor.riskg.Y1 0.051597277 0.094778817
## nodefactor.riskg.Y2 0.008712444 0.009323387
## nodefactor.riskg.Y3 0.024623216 0.023283323
## nodefactor.riskg.Y4 0.053426502 0.074714017
## nodefactor.race..wa.B 0.357320876 0.011459436
## nodefactor.race..wa.H -0.001061294 0.552081496
## nodefactor.region.EW -0.002267354 0.144149364
## nodefactor.region.OW 0.057749803 0.086654166
## nodematch.race..wa.B 1.000000000 -0.010805140
## nodematch.race..wa.H -0.010805140 1.000000000
## nodematch.race..wa.O -0.001337788 -0.013876379
## absdiff.sqrt.age 0.071761524 0.130195938
## nodematch.race..wa.O absdiff.sqrt.age
## edges 0.771143381 0.66328459
## nodefactor.deg.main.deg.pers.0.1 0.485183494 0.37992711
## nodefactor.deg.main.deg.pers.0.2 0.188068117 0.16704498
## nodefactor.deg.main.deg.pers.1.0 0.199257990 0.18020366
## nodefactor.deg.main.deg.pers.1.1 0.387465930 0.32642527
## nodefactor.deg.main.deg.pers.1.2 0.352820111 0.34824833
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 NA NA
## nodefactor.riskg.O4 0.085205807 0.06299155
## nodefactor.riskg.Y1 0.332686607 0.65415205
## nodefactor.riskg.Y2 0.031087260 0.06991716
## nodefactor.riskg.Y3 0.099995382 0.18656533
## nodefactor.riskg.Y4 0.259851116 0.53670170
## nodefactor.race..wa.B -0.001793241 0.31288644
## nodefactor.race..wa.H -0.011658978 0.36290978
## nodefactor.region.EW 0.266012263 0.27235591
## nodefactor.region.OW 0.499581190 0.41679033
## nodematch.race..wa.B -0.001337788 0.07176152
## nodematch.race..wa.H -0.013876379 0.13019594
## nodematch.race..wa.O 1.000000000 0.47879464
## absdiff.sqrt.age 0.478794645 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000 1.0000000
## Lag 1e+05 0.4617516 0.5518739
## Lag 2e+05 0.3620683 0.4463866
## Lag 3e+05 0.2907928 0.3794946
## Lag 4e+05 0.2575937 0.3418394
## Lag 5e+05 0.2451094 0.3234730
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 0.32300413
## Lag 2e+05 0.19371759
## Lag 3e+05 0.12370285
## Lag 4e+05 0.07046152
## Lag 5e+05 0.05072761
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.119818787
## Lag 2e+05 0.025800606
## Lag 3e+05 -0.019405569
## Lag 4e+05 0.008195302
## Lag 5e+05 0.043384663
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000
## Lag 1e+05 0.6083834
## Lag 2e+05 0.5286139
## Lag 3e+05 0.4533106
## Lag 4e+05 0.4066988
## Lag 5e+05 0.3715875
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.0000000 NaN
## Lag 1e+05 0.5680752 NaN
## Lag 2e+05 0.4883576 NaN
## Lag 3e+05 0.4338244 NaN
## Lag 4e+05 0.3925286 NaN
## Lag 5e+05 0.3696824 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 NaN 1.000000000 1.00000000
## Lag 1e+05 NaN 0.003389125 0.09656282
## Lag 2e+05 NaN 0.003150473 0.04648905
## Lag 3e+05 NaN 0.006137694 -0.01535688
## Lag 4e+05 NaN 0.004393475 0.01497092
## Lag 5e+05 NaN -0.021636565 0.01874098
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.001196299 -0.00356037 0.006162418
## Lag 2e+05 0.004789483 0.00547912 0.014343930
## Lag 3e+05 -0.007436475 -0.03529054 0.003418948
## Lag 4e+05 0.039186201 -0.02176417 -0.034488632
## Lag 5e+05 -0.005675322 -0.02284505 -0.006687385
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4770707 0.5337883 0.4670780
## Lag 2e+05 0.3812174 0.4391500 0.3675805
## Lag 3e+05 0.3413962 0.3778181 0.2926384
## Lag 4e+05 0.2980567 0.3450032 0.2721962
## Lag 5e+05 0.2757344 0.3046084 0.2258520
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4058605 0.3961456 0.5786496
## Lag 2e+05 0.2884763 0.3247512 0.4713281
## Lag 3e+05 0.2088593 0.2575536 0.4108832
## Lag 4e+05 0.1594586 0.2281590 0.3787614
## Lag 5e+05 0.1541566 0.1871063 0.3506148
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000 1.000000000
## Lag 1e+05 0.4436619 0.105842202
## Lag 2e+05 0.3273001 0.058469191
## Lag 3e+05 0.2517783 0.008839262
## Lag 4e+05 0.2095882 0.006503340
## Lag 5e+05 0.1994115 0.041146345
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000 1.0000000
## Lag 1e+05 0.4821474 0.5744587
## Lag 2e+05 0.3953206 0.4720723
## Lag 3e+05 0.2988946 0.4194394
## Lag 4e+05 0.2531850 0.3781477
## Lag 5e+05 0.2304493 0.3512590
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 0.33161973
## Lag 2e+05 0.21825766
## Lag 3e+05 0.14205847
## Lag 4e+05 0.10836140
## Lag 5e+05 0.06053335
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.114760114
## Lag 2e+05 0.029990789
## Lag 3e+05 -0.012820680
## Lag 4e+05 0.006458531
## Lag 5e+05 0.021339362
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000
## Lag 1e+05 0.6216545
## Lag 2e+05 0.5344574
## Lag 3e+05 0.4636040
## Lag 4e+05 0.4278899
## Lag 5e+05 0.4021712
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.0000000 NaN
## Lag 1e+05 0.5481514 NaN
## Lag 2e+05 0.4532993 NaN
## Lag 3e+05 0.3821849 NaN
## Lag 4e+05 0.3631613 NaN
## Lag 5e+05 0.3199854 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 NaN 1.00000000 1.0000000000
## Lag 1e+05 NaN 0.02415676 0.0843992636
## Lag 2e+05 NaN -0.02512591 0.0567222981
## Lag 3e+05 NaN -0.01857925 0.0004666716
## Lag 4e+05 NaN -0.01172483 0.0170331598
## Lag 5e+05 NaN -0.03144349 0.0077101741
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.015094806 -0.0346490548 0.062913495
## Lag 2e+05 -0.018536549 -0.0078877440 -0.007132337
## Lag 3e+05 -0.019096506 -0.0062596451 0.005212813
## Lag 4e+05 -0.011658078 -0.0144534005 0.001409406
## Lag 5e+05 0.002436735 0.0007707817 -0.015950152
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4471820 0.5606342 0.4974839
## Lag 2e+05 0.3755829 0.4734767 0.3800522
## Lag 3e+05 0.2999090 0.4015773 0.3092037
## Lag 4e+05 0.2662846 0.3615028 0.2496393
## Lag 5e+05 0.2417790 0.3523887 0.2137153
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4171547 0.4442169 0.6322618
## Lag 2e+05 0.3266620 0.3335384 0.5415132
## Lag 3e+05 0.2436514 0.2906234 0.4821461
## Lag 4e+05 0.1825647 0.2453277 0.4470045
## Lag 5e+05 0.1634603 0.2398299 0.4138281
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000 1.00000000
## Lag 1e+05 0.4682185 0.11315524
## Lag 2e+05 0.3573469 0.06601120
## Lag 3e+05 0.2752419 0.01723359
## Lag 4e+05 0.2373397 0.01650095
## Lag 5e+05 0.1933441 0.01344446
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000 1.0000000
## Lag 1e+05 0.5152878 0.5932103
## Lag 2e+05 0.3933348 0.4908550
## Lag 3e+05 0.3432345 0.4331327
## Lag 4e+05 0.3010877 0.3962899
## Lag 5e+05 0.2659010 0.3365918
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 0.32071243
## Lag 2e+05 0.19649832
## Lag 3e+05 0.12799678
## Lag 4e+05 0.06988926
## Lag 5e+05 0.05086843
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 0.0958434799
## Lag 2e+05 0.0173925705
## Lag 3e+05 0.0417055286
## Lag 4e+05 0.0103444754
## Lag 5e+05 -0.0004609666
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000
## Lag 1e+05 0.5803645
## Lag 2e+05 0.4966492
## Lag 3e+05 0.4476128
## Lag 4e+05 0.4059214
## Lag 5e+05 0.3393920
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.0000000 NaN
## Lag 1e+05 0.5704626 NaN
## Lag 2e+05 0.4730818 NaN
## Lag 3e+05 0.4115392 NaN
## Lag 4e+05 0.3684745 NaN
## Lag 5e+05 0.3320305 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 NaN 1.0000000000 1.00000000
## Lag 1e+05 NaN -0.0092246127 0.09770601
## Lag 2e+05 NaN -0.0082929602 0.01335332
## Lag 3e+05 NaN 0.0073303424 0.04091385
## Lag 4e+05 NaN 0.0001491677 0.05216805
## Lag 5e+05 NaN 0.0065881474 0.01725712
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.013679155 0.033300272 0.035008652
## Lag 2e+05 -0.009798451 0.002661877 0.027248553
## Lag 3e+05 -0.010883237 0.013315023 0.008474923
## Lag 4e+05 -0.037042868 0.011032127 0.007198264
## Lag 5e+05 -0.001913520 0.025266911 0.019969928
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4725789 0.5833735 0.4468567
## Lag 2e+05 0.3541558 0.4936403 0.3226676
## Lag 3e+05 0.3200221 0.4391516 0.2866003
## Lag 4e+05 0.2708024 0.4018216 0.2682385
## Lag 5e+05 0.2341042 0.3528610 0.2263944
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4522453 0.4536758 0.6590132
## Lag 2e+05 0.3430464 0.3519906 0.5645806
## Lag 3e+05 0.2898180 0.3068079 0.5047194
## Lag 4e+05 0.2191590 0.2767216 0.4626625
## Lag 5e+05 0.1910342 0.2450838 0.4340865
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000 1.00000000
## Lag 1e+05 0.4935297 0.13390978
## Lag 2e+05 0.3620186 0.02898096
## Lag 3e+05 0.3075598 0.04359864
## Lag 4e+05 0.2676034 0.04337944
## Lag 5e+05 0.2363159 0.03154929
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000 1.0000000
## Lag 1e+05 0.5257671 0.5821850
## Lag 2e+05 0.4319351 0.4950363
## Lag 3e+05 0.3875049 0.4479013
## Lag 4e+05 0.3399653 0.3944803
## Lag 5e+05 0.3062858 0.3547651
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 0.34983430
## Lag 2e+05 0.22780549
## Lag 3e+05 0.15408861
## Lag 4e+05 0.10475110
## Lag 5e+05 0.07738329
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 0.0968269296
## Lag 2e+05 0.0453161922
## Lag 3e+05 0.0100703778
## Lag 4e+05 0.0016059987
## Lag 5e+05 0.0004893675
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000
## Lag 1e+05 0.5814430
## Lag 2e+05 0.4732159
## Lag 3e+05 0.4288814
## Lag 4e+05 0.3972611
## Lag 5e+05 0.3571744
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.0000000 NaN
## Lag 1e+05 0.6089648 NaN
## Lag 2e+05 0.5289242 NaN
## Lag 3e+05 0.4722774 NaN
## Lag 4e+05 0.4284737 NaN
## Lag 5e+05 0.4010084 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 NaN 1.000000000 1.00000000
## Lag 1e+05 NaN 0.022842798 0.10955827
## Lag 2e+05 NaN 0.005854405 0.04471111
## Lag 3e+05 NaN -0.005114630 0.01809621
## Lag 4e+05 NaN -0.018980118 0.03215190
## Lag 5e+05 NaN -0.011898122 0.02537469
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.003681293 -0.026494778 0.030758223
## Lag 2e+05 0.010543653 0.002259701 0.000223715
## Lag 3e+05 0.008308187 -0.004206008 0.010335788
## Lag 4e+05 -0.028469168 0.013053772 0.005120126
## Lag 5e+05 0.004460180 -0.012944679 -0.004098666
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4876005 0.5991149 0.5107755
## Lag 2e+05 0.4173559 0.5183276 0.4225394
## Lag 3e+05 0.3641339 0.4684762 0.3711443
## Lag 4e+05 0.3379657 0.4154959 0.3184632
## Lag 5e+05 0.3169650 0.3878736 0.2898231
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4342033 0.4536845 0.5921520
## Lag 2e+05 0.3164490 0.3400837 0.4972122
## Lag 3e+05 0.2519794 0.3148857 0.4619920
## Lag 4e+05 0.2259287 0.2717457 0.4038103
## Lag 5e+05 0.2035106 0.2382688 0.3863000
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000 1.000000000
## Lag 1e+05 0.5004109 0.117775544
## Lag 2e+05 0.4081734 0.061832715
## Lag 3e+05 0.3355635 0.056575061
## Lag 4e+05 0.2709065 0.048415038
## Lag 5e+05 0.2572474 0.006347213
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000 1.0000000
## Lag 1e+05 0.5195568 0.6020423
## Lag 2e+05 0.4255347 0.5140198
## Lag 3e+05 0.3522717 0.4604894
## Lag 4e+05 0.3240771 0.4263072
## Lag 5e+05 0.2692905 0.3681916
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 0.32104545
## Lag 2e+05 0.20384397
## Lag 3e+05 0.12751360
## Lag 4e+05 0.07234818
## Lag 5e+05 0.07516452
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.00000000
## Lag 1e+05 0.11439768
## Lag 2e+05 0.01955363
## Lag 3e+05 -0.01356662
## Lag 4e+05 -0.02139613
## Lag 5e+05 0.00427859
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000
## Lag 1e+05 0.5615289
## Lag 2e+05 0.4745475
## Lag 3e+05 0.4109649
## Lag 4e+05 0.3637328
## Lag 5e+05 0.3071199
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.0000000 NaN
## Lag 1e+05 0.5850843 NaN
## Lag 2e+05 0.4965522 NaN
## Lag 3e+05 0.4358503 NaN
## Lag 4e+05 0.3913332 NaN
## Lag 5e+05 0.3566639 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 NaN 1.0000000000 1.000000000
## Lag 1e+05 NaN 0.0100790996 0.106513116
## Lag 2e+05 NaN 0.0047423633 0.046795500
## Lag 3e+05 NaN 0.0327110319 0.001557545
## Lag 4e+05 NaN 0.0101702583 0.008401709
## Lag 5e+05 NaN -0.0004345554 -0.002644899
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.037565294 -0.005040225 0.055244224
## Lag 2e+05 0.009675749 0.008071862 0.038875667
## Lag 3e+05 -0.021627785 0.004790135 0.002192552
## Lag 4e+05 0.002550793 0.010712099 0.021992763
## Lag 5e+05 -0.026619603 0.002025123 -0.010651061
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4828654 0.5459904 0.4879740
## Lag 2e+05 0.3926013 0.4463376 0.3971402
## Lag 3e+05 0.3206475 0.3908246 0.3394830
## Lag 4e+05 0.2754259 0.3489053 0.2944079
## Lag 5e+05 0.2506780 0.3054282 0.2576246
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4812138 0.4754401 0.6109867
## Lag 2e+05 0.3742156 0.3878946 0.5301779
## Lag 3e+05 0.3084831 0.3172372 0.4783160
## Lag 4e+05 0.2621368 0.2658548 0.4370706
## Lag 5e+05 0.2182334 0.2376339 0.4075848
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000 1.000000000
## Lag 1e+05 0.4974287 0.121265544
## Lag 2e+05 0.4042686 0.091192183
## Lag 3e+05 0.3203541 0.034567793
## Lag 4e+05 0.2908213 0.048582396
## Lag 5e+05 0.2401870 0.008585615
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000 1.0000000
## Lag 1e+05 0.4938925 0.5860133
## Lag 2e+05 0.3921216 0.4862437
## Lag 3e+05 0.3375685 0.4311063
## Lag 4e+05 0.3062491 0.3959310
## Lag 5e+05 0.2509886 0.3513956
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 0.35632564
## Lag 2e+05 0.23143366
## Lag 3e+05 0.16554403
## Lag 4e+05 0.13106224
## Lag 5e+05 0.08699888
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.113393266
## Lag 2e+05 0.025868961
## Lag 3e+05 0.009062043
## Lag 4e+05 0.017024158
## Lag 5e+05 -0.010588775
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000
## Lag 1e+05 0.5674350
## Lag 2e+05 0.4758616
## Lag 3e+05 0.4242537
## Lag 4e+05 0.3754652
## Lag 5e+05 0.3259764
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.0000000 NaN
## Lag 1e+05 0.5437857 NaN
## Lag 2e+05 0.4452716 NaN
## Lag 3e+05 0.3808797 NaN
## Lag 4e+05 0.3485841 NaN
## Lag 5e+05 0.3004450 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.032759211 0.076443900
## Lag 2e+05 NaN -0.027812523 0.006727514
## Lag 3e+05 NaN -0.011195242 0.012087149
## Lag 4e+05 NaN -0.007368893 0.022673797
## Lag 5e+05 NaN -0.021428159 0.030502643
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.016625487 -0.003061917 0.026814203
## Lag 2e+05 -0.008302971 -0.005955496 -0.022912729
## Lag 3e+05 -0.001005777 0.010573527 -0.007063925
## Lag 4e+05 0.011614791 -0.042973787 -0.010485888
## Lag 5e+05 0.006017636 0.029215036 -0.016144326
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4788687 0.5717617 0.4659228
## Lag 2e+05 0.3968883 0.4510681 0.3690608
## Lag 3e+05 0.3506905 0.3954648 0.3110669
## Lag 4e+05 0.3314007 0.3357357 0.2850460
## Lag 5e+05 0.2901363 0.2987378 0.2361789
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4438687 0.4370132 0.5966878
## Lag 2e+05 0.3307137 0.3585145 0.4855534
## Lag 3e+05 0.2940443 0.3192846 0.4389891
## Lag 4e+05 0.2512835 0.2959774 0.3833441
## Lag 5e+05 0.1919817 0.2482113 0.3423318
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000 1.00000000
## Lag 1e+05 0.4600429 0.11723619
## Lag 2e+05 0.3561076 0.05529574
## Lag 3e+05 0.3054157 0.04026419
## Lag 4e+05 0.2706260 0.03752732
## Lag 5e+05 0.2331903 0.01924481
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000 1.0000000
## Lag 1e+05 0.4673642 0.5650870
## Lag 2e+05 0.3688911 0.4658896
## Lag 3e+05 0.2850883 0.4113738
## Lag 4e+05 0.2538090 0.3726131
## Lag 5e+05 0.2251903 0.3410495
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 0.33635599
## Lag 2e+05 0.21475565
## Lag 3e+05 0.14623204
## Lag 4e+05 0.11516736
## Lag 5e+05 0.07830631
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.00000000
## Lag 1e+05 0.08552042
## Lag 2e+05 0.06781278
## Lag 3e+05 0.01169976
## Lag 4e+05 0.04449186
## Lag 5e+05 0.01523348
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000
## Lag 1e+05 0.5972407
## Lag 2e+05 0.5154703
## Lag 3e+05 0.4556508
## Lag 4e+05 0.4151433
## Lag 5e+05 0.3845973
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.0000000 NaN
## Lag 1e+05 0.5630397 NaN
## Lag 2e+05 0.4657780 NaN
## Lag 3e+05 0.4126941 NaN
## Lag 4e+05 0.3836983 NaN
## Lag 5e+05 0.3664433 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 NaN 1.0000000000 1.000000000
## Lag 1e+05 NaN -0.0096299025 0.073066812
## Lag 2e+05 NaN 0.0023708220 0.037745506
## Lag 3e+05 NaN -0.0205488936 0.002923169
## Lag 4e+05 NaN -0.0089167181 0.017219212
## Lag 5e+05 NaN -0.0008050061 0.026482111
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.023891654 0.007681857 0.073724153
## Lag 2e+05 -0.000545099 -0.023021061 0.016374001
## Lag 3e+05 0.035292348 0.009585206 0.001133503
## Lag 4e+05 -0.018093112 -0.029347813 -0.009096273
## Lag 5e+05 -0.033075013 -0.002017007 0.004870178
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4641025 0.5596863 0.4520866
## Lag 2e+05 0.3692539 0.4658478 0.3596169
## Lag 3e+05 0.3100468 0.4082629 0.2899433
## Lag 4e+05 0.2707227 0.3602283 0.2541312
## Lag 5e+05 0.2536538 0.3169068 0.2153863
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4087717 0.4321137 0.6532148
## Lag 2e+05 0.2861242 0.3179305 0.5634927
## Lag 3e+05 0.1995025 0.2877844 0.5091594
## Lag 4e+05 0.1755632 0.2568398 0.4849910
## Lag 5e+05 0.1589333 0.2277561 0.4503382
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000 1.00000000
## Lag 1e+05 0.4627009 0.12900456
## Lag 2e+05 0.3612421 0.05784518
## Lag 3e+05 0.2946294 0.01289651
## Lag 4e+05 0.2684787 0.03306910
## Lag 5e+05 0.2233852 0.02882009
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000 1.0000000
## Lag 1e+05 0.4573558 0.5540059
## Lag 2e+05 0.3595517 0.4643069
## Lag 3e+05 0.2931380 0.4013733
## Lag 4e+05 0.2554042 0.3587489
## Lag 5e+05 0.2180891 0.3328862
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 0.33791800
## Lag 2e+05 0.21299766
## Lag 3e+05 0.14950376
## Lag 4e+05 0.08513703
## Lag 5e+05 0.03928050
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.092500132
## Lag 2e+05 0.036998542
## Lag 3e+05 0.001556265
## Lag 4e+05 -0.001900301
## Lag 5e+05 -0.015187593
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000
## Lag 1e+05 0.6110473
## Lag 2e+05 0.5296708
## Lag 3e+05 0.4710341
## Lag 4e+05 0.4455539
## Lag 5e+05 0.3995931
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.0000000 NaN
## Lag 1e+05 0.5214027 NaN
## Lag 2e+05 0.4253006 NaN
## Lag 3e+05 0.3542583 NaN
## Lag 4e+05 0.3390898 NaN
## Lag 5e+05 0.3060891 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.018767196 0.054304723
## Lag 2e+05 NaN -0.019086540 0.041090889
## Lag 3e+05 NaN -0.009851984 0.007148319
## Lag 4e+05 NaN -0.017022255 0.015901542
## Lag 5e+05 NaN -0.033568693 0.015285951
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.001421357 0.007219408 0.029969059
## Lag 2e+05 0.005218810 -0.007846310 0.037026312
## Lag 3e+05 -0.012380537 0.004845966 0.030864363
## Lag 4e+05 0.008230948 0.011931566 0.010252402
## Lag 5e+05 0.011328764 0.002671230 0.003907622
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4778621 0.5568974 0.4779257
## Lag 2e+05 0.3880605 0.4674805 0.3649656
## Lag 3e+05 0.3468027 0.3999599 0.3254291
## Lag 4e+05 0.2859974 0.3624096 0.2733304
## Lag 5e+05 0.2580312 0.3449905 0.2372995
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000 1.0000000 1.0000000
## Lag 1e+05 0.4235133 0.3917729 0.6063778
## Lag 2e+05 0.3069669 0.2876847 0.5201691
## Lag 3e+05 0.2545070 0.2606280 0.4631211
## Lag 4e+05 0.2229850 0.2159464 0.4221047
## Lag 5e+05 0.1736785 0.2002641 0.4217910
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000 1.00000000
## Lag 1e+05 0.4594764 0.10965892
## Lag 2e+05 0.3656525 0.05465696
## Lag 3e+05 0.3009393 0.05243621
## Lag 4e+05 0.2593768 0.04496274
## Lag 5e+05 0.2177785 0.02678074
##
## Sample statistics burn-in diagnostic (Geweke):## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 2.13676 2.45597
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.75124 1.75711
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.02213 0.08728
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.46345 -1.07877
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.45494 0.52695
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.38899 -0.31912
## nodefactor.race..wa.H nodefactor.region.EW
## 0.44513 0.95903
## nodefactor.region.OW nodematch.race..wa.B
## -0.08065 0.25856
## nodematch.race..wa.H nodematch.race..wa.O
## -1.23046 1.81103
## absdiff.sqrt.age
## 0.22718
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.03261736 0.01405054
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.45250861 0.07889948
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.98234500 0.93044544
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.64304486 0.28069134
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.64915592 0.59823058
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.69728072 0.74963651
## nodefactor.race..wa.H nodefactor.region.EW
## 0.65622284 0.33754129
## nodefactor.region.OW nodematch.race..wa.B
## 0.93571810 0.79597411
## nodematch.race..wa.H nodematch.race..wa.O
## 0.21852599 0.07013646
## absdiff.sqrt.age
## 0.82028555
## Joint P-value (lower = worse): 0.01878489 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.12930 0.22174
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.19559 0.64245
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.19670 0.22072
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## -0.32865 0.82481
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.45832 -0.53669
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.18244 0.47076
## nodefactor.race..wa.H nodefactor.region.EW
## 3.33869 2.06597
## nodefactor.region.OW nodematch.race..wa.B
## 0.07601 -0.45331
## nodematch.race..wa.H nodematch.race..wa.O
## 1.43058 -2.55067
## absdiff.sqrt.age
## 0.34001
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.8971229652 0.8245156395
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.2318552352 0.5205839107
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.8440588640 0.8253094741
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.7424174044 0.4094774188
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.6467202956 0.5914796390
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.8552393217 0.6378109088
## nodefactor.race..wa.H nodefactor.region.EW
## 0.0008417479 0.0388316640
## nodefactor.region.OW nodematch.race..wa.B
## 0.9394092693 0.6503226157
## nodematch.race..wa.H nodematch.race..wa.O
## 0.1525498272 0.0107517107
## absdiff.sqrt.age
## 0.7338464416
## Joint P-value (lower = worse): 0.001517838 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.35328 -0.26267
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.37895 -0.88964
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.56285 1.00877
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.24035 0.52374
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.64746 0.11583
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.94382 1.82123
## nodefactor.race..wa.H nodefactor.region.EW
## 0.06649 -0.54917
## nodefactor.region.OW nodematch.race..wa.B
## 0.19058 0.90956
## nodematch.race..wa.H nodematch.race..wa.O
## -1.84054 0.46099
## absdiff.sqrt.age
## 1.62875
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.17596673 0.79280815
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.70472201 0.37365790
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.11808725 0.31308607
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.81005967 0.60045861
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.51733533 0.90779093
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.34526167 0.06857240
## nodefactor.race..wa.H nodefactor.region.EW
## 0.94698975 0.58288658
## nodefactor.region.OW nodematch.race..wa.B
## 0.84885223 0.36305303
## nodematch.race..wa.H nodematch.race..wa.O
## 0.06568968 0.64480460
## absdiff.sqrt.age
## 0.10336535
## Joint P-value (lower = worse): 0.1264758 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.990642 -1.946104
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.084410 1.662902
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.146287 -0.654770
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## -0.048602 0.706129
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -1.544943 -0.602494
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## -1.095205 1.353045
## nodefactor.race..wa.H nodefactor.region.EW
## -2.081894 -0.102797
## nodefactor.region.OW nodematch.race..wa.B
## -0.006502 1.052185
## nodematch.race..wa.H nodematch.race..wa.O
## -1.044548 -0.908841
## absdiff.sqrt.age
## -0.927505
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.32186061 0.05164230
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.93273015 0.09633197
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.88369464 0.51261590
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.96123639 0.48010780
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.12236009 0.54684523
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.27342666 0.17604123
## nodefactor.race..wa.H nodefactor.region.EW
## 0.03735214 0.91812370
## nodefactor.region.OW nodematch.race..wa.B
## 0.99481205 0.29271473
## nodematch.race..wa.H nodematch.race..wa.O
## 0.29623177 0.36343401
## absdiff.sqrt.age
## 0.35366435
## Joint P-value (lower = worse): 0.003043613 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.0001959 0.3589916
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -2.1822845 0.2266491
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.7056280 -1.5041554
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## -1.3088271 -1.3590552
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.1366454 -1.6819904
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.0858141 1.0014140
## nodefactor.race..wa.H nodefactor.region.EW
## -1.4676330 1.5055534
## nodefactor.region.OW nodematch.race..wa.B
## 0.4557819 3.1260093
## nodematch.race..wa.H nodematch.race..wa.O
## -1.7082533 0.5384897
## absdiff.sqrt.age
## -0.5674052
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.999843658 0.719601383
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.029088541 0.820696577
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.088077370 0.132541367
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.190592924 0.174129103
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.891311128 0.092570696
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.931614203 0.316626686
## nodefactor.race..wa.H nodefactor.region.EW
## 0.142203930 0.132181868
## nodefactor.region.OW nodematch.race..wa.B
## 0.648546814 0.001771959
## nodematch.race..wa.H nodematch.race..wa.O
## 0.087589344 0.590238980
## absdiff.sqrt.age
## 0.570438889
## Joint P-value (lower = worse): 0.1912513 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.18748 0.20714
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -2.28489 -1.45104
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -1.62107 0.48803
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.09541 -1.18931
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.36695 -0.74309
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## -1.70663 -1.44838
## nodefactor.race..wa.H nodefactor.region.EW
## 1.22552 -0.24523
## nodefactor.region.OW nodematch.race..wa.B
## -0.92698 0.52222
## nodematch.race..wa.H nodematch.race..wa.O
## -0.93297 -0.15354
## absdiff.sqrt.age
## -1.62598
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.85128073 0.83590137
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.02231900 0.14676753
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.10500293 0.62553108
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.92398895 0.23431725
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.71365413 0.45743005
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.08789055 0.14750957
## nodefactor.race..wa.H nodefactor.region.EW
## 0.22037867 0.80627546
## nodefactor.region.OW nodematch.race..wa.B
## 0.35393454 0.60151591
## nodematch.race..wa.H nodematch.race..wa.O
## 0.35083716 0.87797147
## absdiff.sqrt.age
## 0.10395325
## Joint P-value (lower = worse): 0.004604569 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.19266 1.19752
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.33376 1.14553
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.23465 1.38073
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 2.23418 -1.04931
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.96255 0.85054
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.52199 -0.50498
## nodefactor.race..wa.H nodefactor.region.EW
## 0.51297 2.67785
## nodefactor.region.OW nodematch.race..wa.B
## 0.17015 1.66730
## nodematch.race..wa.H nodematch.race..wa.O
## 0.46735 0.86519
## absdiff.sqrt.age
## -0.05732
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.233003786 0.231105001
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.738560684 0.251989029
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.814478366 0.167362694
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.025471225 0.294034998
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.335773293 0.395023401
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.601674803 0.613572447
## nodefactor.race..wa.H nodefactor.region.EW
## 0.607971572 0.007409687
## nodefactor.region.OW nodematch.race..wa.B
## 0.864891659 0.095454577
## nodematch.race..wa.H nodematch.race..wa.O
## 0.640246737 0.386934285
## absdiff.sqrt.age
## 0.954289171
## Joint P-value (lower = worse): 0.9550141 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -3.74003 -0.80572
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.36613 -0.83924
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -2.91083 -1.12220
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## -0.54124 0.05853
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.19901 1.65029
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## -2.98968 -2.45656
## nodefactor.race..wa.H nodefactor.region.EW
## -2.43892 -2.41813
## nodefactor.region.OW nodematch.race..wa.B
## -0.04881 -0.89159
## nodematch.race..wa.H nodematch.race..wa.O
## 1.06496 -1.30163
## absdiff.sqrt.age
## -1.29098
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.0001839947 0.4204056942
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.7142686365 0.4013352224
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.0036047146 0.2617753438
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN NaN
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.5883391311 0.9533229246
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.8422551796 0.0988829642
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.0027927406 0.0140276284
## nodefactor.race..wa.H nodefactor.region.EW
## 0.0147313142 0.0156003457
## nodefactor.region.OW nodematch.race..wa.B
## 0.9610722046 0.3726129809
## nodematch.race..wa.H nodematch.race..wa.O
## 0.2868945416 0.1930423411
## absdiff.sqrt.age
## 0.1967120796
## Joint P-value (lower = worse): 0.01161936 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 8
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 4.00168 21.958 0.126775 0.172473
## nodefactor.deg.main.deg.pers.0.1 1.94433 14.421 0.083260 0.136694
## nodefactor.deg.main.deg.pers.0.2 0.61357 6.190 0.035737 0.037537
## nodefactor.deg.main.deg.pers.1.0 0.03950 6.290 0.036316 0.036467
## nodefactor.deg.main.deg.pers.1.1 0.18640 12.376 0.071455 0.115903
## nodefactor.deg.main.deg.pers.1.2 0.52934 12.982 0.074954 0.117498
## nodefactor.riskg.O1 -0.40092 0.000 0.000000 0.000000
## nodefactor.riskg.O2 -0.40092 0.000 0.000000 0.000000
## nodefactor.riskg.O3 0.46142 2.725 0.015734 0.015594
## nodefactor.riskg.O4 0.94690 11.733 0.067743 0.077297
## nodefactor.riskg.Y1 0.04566 1.181 0.006816 0.006888
## nodefactor.riskg.Y2 0.04176 2.882 0.016640 0.016671
## nodefactor.riskg.Y3 0.07850 8.718 0.050334 0.051036
## nodefactor.race..wa.B 3.41338 9.381 0.054163 0.077357
## nodefactor.race..wa.H 0.89948 13.203 0.076228 0.120168
## nodefactor.region.EW 0.54889 11.156 0.064408 0.102806
## nodefactor.region.OW 3.38289 20.502 0.118367 0.153351
## nodematch.race..wa.B 1.72468 2.051 0.011843 0.017389
## nodematch.race..wa.H 0.14125 3.657 0.021111 0.040598
## nodematch.race..wa.O 1.40561 16.855 0.097313 0.127641
## nodematch.region 3.27894 19.671 0.113571 0.165194
## absdiff.sqrt.age 3.40263 22.526 0.130055 0.155884
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75%
## edges -39.1586 -11.1586 3.84138 18.8414
## nodefactor.deg.main.deg.pers.0.1 -25.3100 -8.3100 1.68996 11.6900
## nodefactor.deg.main.deg.pers.0.2 -11.3710 -3.3710 0.62897 4.6290
## nodefactor.deg.main.deg.pers.1.0 -12.0335 -4.0335 -0.03347 3.9665
## nodefactor.deg.main.deg.pers.1.1 -23.5379 -8.5379 -0.53786 8.4621
## nodefactor.deg.main.deg.pers.1.2 -24.3881 -8.3881 0.61188 9.6119
## nodefactor.riskg.O1 -0.4009 -0.4009 -0.40092 -0.4009
## nodefactor.riskg.O2 -0.4009 -0.4009 -0.40092 -0.4009
## nodefactor.riskg.O3 -4.8558 -1.8558 0.14418 2.1442
## nodefactor.riskg.O4 -21.5127 -7.5127 0.48734 8.4873
## nodefactor.riskg.Y1 -1.3491 -0.3491 -0.34908 0.6509
## nodefactor.riskg.Y2 -5.2024 -2.2024 -0.20238 1.7976
## nodefactor.riskg.Y3 -16.7860 -5.7860 0.21403 6.2140
## nodefactor.race..wa.B -14.5908 -2.5908 3.40918 9.4092
## nodefactor.race..wa.H -24.1739 -8.1739 0.82608 9.8261
## nodefactor.region.EW -20.5014 -7.5014 0.49862 7.4986
## nodefactor.region.OW -35.4862 -10.4862 2.51379 17.5138
## nodematch.race..wa.B -1.5399 0.4601 1.46015 3.4601
## nodematch.race..wa.H -6.2690 -2.2690 -0.26902 2.7310
## nodematch.race..wa.O -30.8800 -9.8800 1.11998 13.1200
## nodematch.region -35.3269 -10.3269 2.67310 16.6731
## absdiff.sqrt.age -39.8863 -12.0691 3.10443 18.7067
## 97.5%
## edges 46.8414
## nodefactor.deg.main.deg.pers.0.1 30.6900
## nodefactor.deg.main.deg.pers.0.2 13.6290
## nodefactor.deg.main.deg.pers.1.0 12.9665
## nodefactor.deg.main.deg.pers.1.1 25.4621
## nodefactor.deg.main.deg.pers.1.2 26.6119
## nodefactor.riskg.O1 -0.4009
## nodefactor.riskg.O2 -0.4009
## nodefactor.riskg.O3 6.1442
## nodefactor.riskg.O4 24.4873
## nodefactor.riskg.Y1 2.6509
## nodefactor.riskg.Y2 5.7976
## nodefactor.riskg.Y3 17.2140
## nodefactor.race..wa.B 22.4092
## nodefactor.race..wa.H 27.8261
## nodefactor.region.EW 22.4986
## nodefactor.region.OW 44.5138
## nodematch.race..wa.B 6.4601
## nodematch.race..wa.H 7.7310
## nodematch.race..wa.O 35.1200
## nodematch.region 41.6731
## absdiff.sqrt.age 48.1163
##
##
## Sample statistics cross-correlations:## Warning in cor(as.matrix(x)): the standard deviation is zero## edges
## edges 1.00000000
## nodefactor.deg.main.deg.pers.0.1 0.55075590
## nodefactor.deg.main.deg.pers.0.2 0.27608827
## nodefactor.deg.main.deg.pers.1.0 0.27528881
## nodefactor.deg.main.deg.pers.1.1 0.49329541
## nodefactor.deg.main.deg.pers.1.2 0.51033663
## nodefactor.riskg.O1 NA
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.11782879
## nodefactor.riskg.O4 0.42653991
## nodefactor.riskg.Y1 0.05491746
## nodefactor.riskg.Y2 0.12323439
## nodefactor.riskg.Y3 0.37518185
## nodefactor.race..wa.B 0.38881286
## nodefactor.race..wa.H 0.51553427
## nodefactor.region.EW 0.35048771
## nodefactor.region.OW 0.54655894
## nodematch.race..wa.B 0.09376176
## nodematch.race..wa.H 0.16407190
## nodematch.race..wa.O 0.77489365
## nodematch.region 0.89452739
## absdiff.sqrt.age 0.77273964
## nodefactor.deg.main.deg.pers.0.1
## edges 0.55075590
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.07682811
## nodefactor.deg.main.deg.pers.1.0 0.06606833
## nodefactor.deg.main.deg.pers.1.1 0.12864151
## nodefactor.deg.main.deg.pers.1.2 0.13952608
## nodefactor.riskg.O1 NA
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.07390317
## nodefactor.riskg.O4 0.26325315
## nodefactor.riskg.Y1 0.04226189
## nodefactor.riskg.Y2 0.06884458
## nodefactor.riskg.Y3 0.21878302
## nodefactor.race..wa.B 0.24213344
## nodefactor.race..wa.H 0.24199672
## nodefactor.region.EW 0.19783614
## nodefactor.region.OW 0.36906869
## nodematch.race..wa.B 0.06572864
## nodematch.race..wa.H 0.06805920
## nodematch.race..wa.O 0.44012685
## nodematch.region 0.48436129
## absdiff.sqrt.age 0.43536615
## nodefactor.deg.main.deg.pers.0.2
## edges 0.27608827
## nodefactor.deg.main.deg.pers.0.1 0.07682811
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.03984259
## nodefactor.deg.main.deg.pers.1.1 0.06280018
## nodefactor.deg.main.deg.pers.1.2 0.07228193
## nodefactor.riskg.O1 NA
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.03334999
## nodefactor.riskg.O4 0.12833619
## nodefactor.riskg.Y1 0.01026939
## nodefactor.riskg.Y2 0.03411059
## nodefactor.riskg.Y3 0.09148966
## nodefactor.race..wa.B 0.09615413
## nodefactor.race..wa.H 0.11801351
## nodefactor.region.EW 0.07037202
## nodefactor.region.OW 0.14462320
## nodematch.race..wa.B 0.01120024
## nodematch.race..wa.H 0.03259363
## nodematch.race..wa.O 0.23394314
## nodematch.region 0.25383212
## absdiff.sqrt.age 0.21556928
## nodefactor.deg.main.deg.pers.1.0
## edges 0.275288813
## nodefactor.deg.main.deg.pers.0.1 0.066068334
## nodefactor.deg.main.deg.pers.0.2 0.039842593
## nodefactor.deg.main.deg.pers.1.0 1.000000000
## nodefactor.deg.main.deg.pers.1.1 0.063681735
## nodefactor.deg.main.deg.pers.1.2 0.079593459
## nodefactor.riskg.O1 NA
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.031804968
## nodefactor.riskg.O4 0.120978159
## nodefactor.riskg.Y1 0.014080572
## nodefactor.riskg.Y2 0.024817267
## nodefactor.riskg.Y3 0.104142210
## nodefactor.race..wa.B 0.080441570
## nodefactor.race..wa.H 0.148436208
## nodefactor.region.EW 0.093541007
## nodefactor.region.OW 0.123507225
## nodematch.race..wa.B 0.007982736
## nodematch.race..wa.H 0.052532645
## nodematch.race..wa.O 0.220077697
## nodematch.region 0.247021964
## absdiff.sqrt.age 0.213436497
## nodefactor.deg.main.deg.pers.1.1
## edges 0.49329541
## nodefactor.deg.main.deg.pers.0.1 0.12864151
## nodefactor.deg.main.deg.pers.0.2 0.06280018
## nodefactor.deg.main.deg.pers.1.0 0.06368173
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.13606934
## nodefactor.riskg.O1 NA
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.04180244
## nodefactor.riskg.O4 0.20370595
## nodefactor.riskg.Y1 0.01104443
## nodefactor.riskg.Y2 0.06567203
## nodefactor.riskg.Y3 0.17643858
## nodefactor.race..wa.B 0.14976788
## nodefactor.race..wa.H 0.32674342
## nodefactor.region.EW 0.23182729
## nodefactor.region.OW 0.18090767
## nodematch.race..wa.B 0.02620569
## nodematch.race..wa.H 0.13103811
## nodematch.race..wa.O 0.35937207
## nodematch.region 0.44311805
## absdiff.sqrt.age 0.38444712
## nodefactor.deg.main.deg.pers.1.2
## edges 0.51033663
## nodefactor.deg.main.deg.pers.0.1 0.13952608
## nodefactor.deg.main.deg.pers.0.2 0.07228193
## nodefactor.deg.main.deg.pers.1.0 0.07959346
## nodefactor.deg.main.deg.pers.1.1 0.13606934
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.riskg.O1 NA
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.06778756
## nodefactor.riskg.O4 0.15987650
## nodefactor.riskg.Y1 0.02665306
## nodefactor.riskg.Y2 0.06375236
## nodefactor.riskg.Y3 0.19918254
## nodefactor.race..wa.B 0.14116976
## nodefactor.race..wa.H 0.27880833
## nodefactor.region.EW 0.15601339
## nodefactor.region.OW 0.25405279
## nodematch.race..wa.B 0.01949303
## nodematch.race..wa.H 0.09905141
## nodematch.race..wa.O 0.40961070
## nodematch.region 0.46190060
## absdiff.sqrt.age 0.39012282
## nodefactor.riskg.O1 nodefactor.riskg.O2
## edges NA NA
## nodefactor.deg.main.deg.pers.0.1 NA NA
## nodefactor.deg.main.deg.pers.0.2 NA NA
## nodefactor.deg.main.deg.pers.1.0 NA NA
## nodefactor.deg.main.deg.pers.1.1 NA NA
## nodefactor.deg.main.deg.pers.1.2 NA NA
## nodefactor.riskg.O1 1 NA
## nodefactor.riskg.O2 NA 1
## nodefactor.riskg.O3 NA NA
## nodefactor.riskg.O4 NA NA
## nodefactor.riskg.Y1 NA NA
## nodefactor.riskg.Y2 NA NA
## nodefactor.riskg.Y3 NA NA
## nodefactor.race..wa.B NA NA
## nodefactor.race..wa.H NA NA
## nodefactor.region.EW NA NA
## nodefactor.region.OW NA NA
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H NA NA
## nodematch.race..wa.O NA NA
## nodematch.region NA NA
## absdiff.sqrt.age NA NA
## nodefactor.riskg.O3 nodefactor.riskg.O4
## edges 0.1178287947 0.426539913
## nodefactor.deg.main.deg.pers.0.1 0.0739031745 0.263253146
## nodefactor.deg.main.deg.pers.0.2 0.0333499891 0.128336192
## nodefactor.deg.main.deg.pers.1.0 0.0318049681 0.120978159
## nodefactor.deg.main.deg.pers.1.1 0.0418024356 0.203705948
## nodefactor.deg.main.deg.pers.1.2 0.0677875641 0.159876497
## nodefactor.riskg.O1 NA NA
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 1.0000000000 0.052219944
## nodefactor.riskg.O4 0.0522199436 1.000000000
## nodefactor.riskg.Y1 0.0079808336 0.008051066
## nodefactor.riskg.Y2 0.0034826690 0.017957512
## nodefactor.riskg.Y3 0.0122959653 0.070041188
## nodefactor.race..wa.B 0.0302689339 0.134322876
## nodefactor.race..wa.H 0.0560046184 0.253434579
## nodefactor.region.EW 0.0308941542 0.128029743
## nodefactor.region.OW 0.0690318563 0.224004814
## nodematch.race..wa.B 0.0006287124 0.020691195
## nodematch.race..wa.H 0.0114253940 0.092919342
## nodematch.race..wa.O 0.0990768055 0.323297164
## nodematch.region 0.1092513209 0.382918530
## absdiff.sqrt.age 0.1235793938 0.420800666
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## edges 0.054917456 0.123234387
## nodefactor.deg.main.deg.pers.0.1 0.042261889 0.068844581
## nodefactor.deg.main.deg.pers.0.2 0.010269387 0.034110593
## nodefactor.deg.main.deg.pers.1.0 0.014080572 0.024817267
## nodefactor.deg.main.deg.pers.1.1 0.011044431 0.065672034
## nodefactor.deg.main.deg.pers.1.2 0.026653062 0.063752364
## nodefactor.riskg.O1 NA NA
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 0.007980834 0.003482669
## nodefactor.riskg.O4 0.008051066 0.017957512
## nodefactor.riskg.Y1 1.000000000 0.004055536
## nodefactor.riskg.Y2 0.004055536 1.000000000
## nodefactor.riskg.Y3 0.003248239 0.020609561
## nodefactor.race..wa.B 0.009005634 0.040437021
## nodefactor.race..wa.H 0.019183802 0.068615233
## nodefactor.region.EW 0.011028986 0.031903756
## nodefactor.region.OW 0.025195328 0.063828978
## nodematch.race..wa.B 0.007217855 0.006160376
## nodematch.race..wa.H 0.006297866 0.027428254
## nodematch.race..wa.O 0.052897636 0.094722709
## nodematch.region 0.049048729 0.113387118
## absdiff.sqrt.age 0.037454893 0.089467573
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## edges 0.375181855 0.388812861
## nodefactor.deg.main.deg.pers.0.1 0.218783024 0.242133441
## nodefactor.deg.main.deg.pers.0.2 0.091489665 0.096154131
## nodefactor.deg.main.deg.pers.1.0 0.104142210 0.080441570
## nodefactor.deg.main.deg.pers.1.1 0.176438578 0.149767879
## nodefactor.deg.main.deg.pers.1.2 0.199182536 0.141169762
## nodefactor.riskg.O1 NA NA
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 0.012295965 0.030268934
## nodefactor.riskg.O4 0.070041188 0.134322876
## nodefactor.riskg.Y1 0.003248239 0.009005634
## nodefactor.riskg.Y2 0.020609561 0.040437021
## nodefactor.riskg.Y3 1.000000000 0.129640514
## nodefactor.race..wa.B 0.129640514 1.000000000
## nodefactor.race..wa.H 0.178950837 0.122157151
## nodefactor.region.EW 0.125226500 0.082151599
## nodefactor.region.OW 0.215387217 0.217848750
## nodematch.race..wa.B 0.030706343 0.440985921
## nodematch.race..wa.H 0.051013278 -0.020769026
## nodematch.race..wa.O 0.306448339 0.009635829
## nodematch.region 0.341846962 0.348633856
## absdiff.sqrt.age 0.277793807 0.299574260
## nodefactor.race..wa.H
## edges 0.515534266
## nodefactor.deg.main.deg.pers.0.1 0.241996718
## nodefactor.deg.main.deg.pers.0.2 0.118013511
## nodefactor.deg.main.deg.pers.1.0 0.148436208
## nodefactor.deg.main.deg.pers.1.1 0.326743421
## nodefactor.deg.main.deg.pers.1.2 0.278808328
## nodefactor.riskg.O1 NA
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.056004618
## nodefactor.riskg.O4 0.253434579
## nodefactor.riskg.Y1 0.019183802
## nodefactor.riskg.Y2 0.068615233
## nodefactor.riskg.Y3 0.178950837
## nodefactor.race..wa.B 0.122157151
## nodefactor.race..wa.H 1.000000000
## nodefactor.region.EW 0.278388507
## nodefactor.region.OW 0.304500797
## nodematch.race..wa.B -0.003224104
## nodematch.race..wa.H 0.546687141
## nodematch.race..wa.O 0.010098022
## nodematch.region 0.444571251
## absdiff.sqrt.age 0.402106984
## nodefactor.region.EW nodefactor.region.OW
## edges 0.35048771 0.54655894
## nodefactor.deg.main.deg.pers.0.1 0.19783614 0.36906869
## nodefactor.deg.main.deg.pers.0.2 0.07037202 0.14462320
## nodefactor.deg.main.deg.pers.1.0 0.09354101 0.12350723
## nodefactor.deg.main.deg.pers.1.1 0.23182729 0.18090767
## nodefactor.deg.main.deg.pers.1.2 0.15601339 0.25405279
## nodefactor.riskg.O1 NA NA
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 0.03089415 0.06903186
## nodefactor.riskg.O4 0.12802974 0.22400481
## nodefactor.riskg.Y1 0.01102899 0.02519533
## nodefactor.riskg.Y2 0.03190376 0.06382898
## nodefactor.riskg.Y3 0.12522650 0.21538722
## nodefactor.race..wa.B 0.08215160 0.21784875
## nodefactor.race..wa.H 0.27838851 0.30450080
## nodefactor.region.EW 1.00000000 0.07245029
## nodefactor.region.OW 0.07245029 1.00000000
## nodematch.race..wa.B 0.01360395 0.05382906
## nodematch.race..wa.H 0.13007125 0.09569426
## nodematch.race..wa.O 0.23846890 0.40788916
## nodematch.region 0.20439483 0.43987585
## absdiff.sqrt.age 0.26411066 0.41498479
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.0937617580 0.164071903
## nodefactor.deg.main.deg.pers.0.1 0.0657286381 0.068059203
## nodefactor.deg.main.deg.pers.0.2 0.0112002445 0.032593633
## nodefactor.deg.main.deg.pers.1.0 0.0079827362 0.052532645
## nodefactor.deg.main.deg.pers.1.1 0.0262056907 0.131038113
## nodefactor.deg.main.deg.pers.1.2 0.0194930349 0.099051405
## nodefactor.riskg.O1 NA NA
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 0.0006287124 0.011425394
## nodefactor.riskg.O4 0.0206911950 0.092919342
## nodefactor.riskg.Y1 0.0072178551 0.006297866
## nodefactor.riskg.Y2 0.0061603757 0.027428254
## nodefactor.riskg.Y3 0.0307063425 0.051013278
## nodefactor.race..wa.B 0.4409859210 -0.020769026
## nodefactor.race..wa.H -0.0032241038 0.546687141
## nodefactor.region.EW 0.0136039464 0.130071251
## nodefactor.region.OW 0.0538290553 0.095694259
## nodematch.race..wa.B 1.0000000000 -0.004350296
## nodematch.race..wa.H -0.0043502959 1.000000000
## nodematch.race..wa.O 0.0028568926 0.010547587
## nodematch.region 0.0868624510 0.139435840
## absdiff.sqrt.age 0.0782004881 0.128405685
## nodematch.race..wa.O nodematch.region
## edges 0.774893649 0.89452739
## nodefactor.deg.main.deg.pers.0.1 0.440126851 0.48436129
## nodefactor.deg.main.deg.pers.0.2 0.233943139 0.25383212
## nodefactor.deg.main.deg.pers.1.0 0.220077697 0.24702196
## nodefactor.deg.main.deg.pers.1.1 0.359372075 0.44311805
## nodefactor.deg.main.deg.pers.1.2 0.409610702 0.46190060
## nodefactor.riskg.O1 NA NA
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 0.099076805 0.10925132
## nodefactor.riskg.O4 0.323297164 0.38291853
## nodefactor.riskg.Y1 0.052897636 0.04904873
## nodefactor.riskg.Y2 0.094722709 0.11338712
## nodefactor.riskg.Y3 0.306448339 0.34184696
## nodefactor.race..wa.B 0.009635829 0.34863386
## nodefactor.race..wa.H 0.010098022 0.44457125
## nodefactor.region.EW 0.238468899 0.20439483
## nodefactor.region.OW 0.407889157 0.43987585
## nodematch.race..wa.B 0.002856893 0.08686245
## nodematch.race..wa.H 0.010547587 0.13943584
## nodematch.race..wa.O 1.000000000 0.70297329
## nodematch.region 0.702973293 1.00000000
## absdiff.sqrt.age 0.597908133 0.69272931
## absdiff.sqrt.age
## edges 0.77273964
## nodefactor.deg.main.deg.pers.0.1 0.43536615
## nodefactor.deg.main.deg.pers.0.2 0.21556928
## nodefactor.deg.main.deg.pers.1.0 0.21343650
## nodefactor.deg.main.deg.pers.1.1 0.38444712
## nodefactor.deg.main.deg.pers.1.2 0.39012282
## nodefactor.riskg.O1 NA
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.12357939
## nodefactor.riskg.O4 0.42080067
## nodefactor.riskg.Y1 0.03745489
## nodefactor.riskg.Y2 0.08946757
## nodefactor.riskg.Y3 0.27779381
## nodefactor.race..wa.B 0.29957426
## nodefactor.race..wa.H 0.40210698
## nodefactor.region.EW 0.26411066
## nodefactor.region.OW 0.41498479
## nodematch.race..wa.B 0.07820049
## nodematch.race..wa.H 0.12840568
## nodematch.race..wa.O 0.59790813
## nodematch.region 0.69272931
## absdiff.sqrt.age 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.20129993 0.31491845
## Lag 2e+05 0.09192211 0.18248211
## Lag 3e+05 0.07455162 0.10941162
## Lag 4e+05 0.04433054 0.06415864
## Lag 5e+05 0.03810776 0.05340657
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.044010539
## Lag 2e+05 -0.006381757
## Lag 3e+05 0.004965846
## Lag 4e+05 -0.012823802
## Lag 5e+05 0.012648649
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.031346517
## Lag 2e+05 -0.003480235
## Lag 3e+05 0.014006528
## Lag 4e+05 0.003096910
## Lag 5e+05 -0.018431106
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.33665437
## Lag 2e+05 0.18166916
## Lag 3e+05 0.13860459
## Lag 4e+05 0.10485688
## Lag 5e+05 0.06166205
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.30003707 NaN
## Lag 2e+05 0.17021833 NaN
## Lag 3e+05 0.12486357 NaN
## Lag 4e+05 0.05685996 NaN
## Lag 5e+05 0.05523928 NaN
## nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0 NaN 1.000000000 1.00000000
## Lag 1e+05 NaN -0.012079595 0.10855129
## Lag 2e+05 NaN 0.019493246 0.03132515
## Lag 3e+05 NaN -0.009533403 0.05456121
## Lag 4e+05 NaN -0.014404772 0.02243770
## Lag 5e+05 NaN -0.025252217 0.02664785
## nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.007813566 -0.0044620383 -0.020195977
## Lag 2e+05 -0.019929844 0.0438982115 -0.004298346
## Lag 3e+05 0.020459452 0.0009948979 -0.012219839
## Lag 4e+05 0.035704660 -0.0307580042 0.007501289
## Lag 5e+05 0.033660902 -0.0324136717 0.013721945
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.20994847 0.28506057 0.26779707
## Lag 2e+05 0.09485882 0.16483074 0.15729315
## Lag 3e+05 0.07772897 0.12237209 0.10489483
## Lag 4e+05 0.06123782 0.07289491 0.06876030
## Lag 5e+05 0.05121063 0.04227332 0.05695622
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000
## Lag 1e+05 0.179976352 0.212394287 0.3765100
## Lag 2e+05 0.088977676 0.098737482 0.2731099
## Lag 3e+05 0.068387847 0.090698080 0.2130957
## Lag 4e+05 0.049155556 0.036838165 0.1549889
## Lag 5e+05 -0.006275841 0.002159086 0.1016407
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.19805419 0.25254939 0.10689701
## Lag 2e+05 0.09947109 0.11353291 0.04443078
## Lag 3e+05 0.05566930 0.07733932 0.04072907
## Lag 4e+05 0.04534273 0.05309509 0.04513220
## Lag 5e+05 0.03350735 0.04574283 0.01847718
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.18388150 0.32355123
## Lag 2e+05 0.08526363 0.19150208
## Lag 3e+05 0.04602199 0.12733872
## Lag 4e+05 0.02648347 0.07395077
## Lag 5e+05 -0.01538669 0.02461530
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.036956381
## Lag 2e+05 -0.005318630
## Lag 3e+05 -0.003425924
## Lag 4e+05 0.012687655
## Lag 5e+05 -0.002604130
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.012620226
## Lag 2e+05 -0.010653290
## Lag 3e+05 -0.006287132
## Lag 4e+05 -0.002058960
## Lag 5e+05 -0.003371775
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.31715831
## Lag 2e+05 0.15927738
## Lag 3e+05 0.14183558
## Lag 4e+05 0.09610526
## Lag 5e+05 0.07650321
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.29581692 NaN
## Lag 2e+05 0.14373371 NaN
## Lag 3e+05 0.08125857 NaN
## Lag 4e+05 0.05168590 NaN
## Lag 5e+05 0.02069235 NaN
## nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0 NaN 1.0000000000 1.000000000
## Lag 1e+05 NaN -0.0509709084 0.067533140
## Lag 2e+05 NaN 0.0172206271 0.032721552
## Lag 3e+05 NaN -0.0001849933 0.007938125
## Lag 4e+05 NaN -0.0379547556 0.036701369
## Lag 5e+05 NaN 0.0281328602 0.008961596
## nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.028847232 0.007455700 0.014074056
## Lag 2e+05 0.037771097 -0.008180933 -0.009681983
## Lag 3e+05 -0.018654834 -0.005415666 -0.005512152
## Lag 4e+05 -0.009666338 -0.011719121 0.009589900
## Lag 5e+05 -0.017350602 0.006908394 0.001264134
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.21743383 0.27597940 0.26558438
## Lag 2e+05 0.11394589 0.15357584 0.15192521
## Lag 3e+05 0.05055204 0.12566279 0.07558147
## Lag 4e+05 0.04228884 0.07613763 0.06568469
## Lag 5e+05 0.03031518 0.04226778 0.05403955
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.192036558 0.25541079 0.34907442
## Lag 2e+05 0.090557587 0.14065901 0.22980886
## Lag 3e+05 0.055057943 0.07141031 0.13374358
## Lag 4e+05 0.009604263 0.07059090 0.11065761
## Lag 5e+05 -0.006178966 0.04720275 0.06170796
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.19194341 0.22532472 0.10088315
## Lag 2e+05 0.09553458 0.09751775 0.05022287
## Lag 3e+05 0.04361920 0.04978586 0.04871868
## Lag 4e+05 0.05740083 0.02560889 0.02397800
## Lag 5e+05 0.01025768 -0.01858163 -0.01114386
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.19099306 0.31394181
## Lag 2e+05 0.08666463 0.18550314
## Lag 3e+05 0.03541982 0.12274087
## Lag 4e+05 0.04276316 0.07283654
## Lag 5e+05 0.03688249 0.03865061
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.041039462
## Lag 2e+05 -0.003906875
## Lag 3e+05 0.001235230
## Lag 4e+05 -0.020579219
## Lag 5e+05 0.007290548
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.037010830
## Lag 2e+05 -0.021914329
## Lag 3e+05 -0.010457318
## Lag 4e+05 -0.004992336
## Lag 5e+05 0.007753687
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.30284169
## Lag 2e+05 0.16415078
## Lag 3e+05 0.10112971
## Lag 4e+05 0.09915251
## Lag 5e+05 0.05661441
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.30931148 NaN
## Lag 2e+05 0.17016238 NaN
## Lag 3e+05 0.09366197 NaN
## Lag 4e+05 0.07628022 NaN
## Lag 5e+05 0.07332734 NaN
## nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0 NaN 1.0000000000 1.0000000000
## Lag 1e+05 NaN 0.0003232322 0.1110566044
## Lag 2e+05 NaN -0.0141797992 0.0265608988
## Lag 3e+05 NaN 0.0006166321 0.0164142620
## Lag 4e+05 NaN 0.0033967501 -0.0001728478
## Lag 5e+05 NaN -0.0124968367 0.0025765887
## nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.010173310 -0.015743026 0.006577369
## Lag 2e+05 0.019949399 0.008141449 -0.016195106
## Lag 3e+05 0.022639347 0.001144627 -0.025991440
## Lag 4e+05 -0.023194769 -0.034644483 0.020933308
## Lag 5e+05 0.003796121 -0.003652207 0.041496383
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.196306656 0.27438539 0.23375365
## Lag 2e+05 0.078773151 0.15230879 0.13239218
## Lag 3e+05 0.026621830 0.08693521 0.08730386
## Lag 4e+05 0.005144658 0.07761731 0.06546696
## Lag 5e+05 0.012521181 0.06285869 0.04066496
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.19871703 0.27093868 0.38762181
## Lag 2e+05 0.06586543 0.13744210 0.23406586
## Lag 3e+05 0.02644668 0.08958688 0.17067470
## Lag 4e+05 0.02938314 0.04598522 0.12955626
## Lag 5e+05 0.01175612 0.01544252 0.07786773
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.16943041 0.23859417 0.094788020
## Lag 2e+05 0.04522350 0.12558999 0.050654085
## Lag 3e+05 0.04783009 0.06057468 0.013602579
## Lag 4e+05 0.02820850 0.03995263 -0.000891723
## Lag 5e+05 0.01491930 0.04127578 0.017558434
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.20381970 0.34493185
## Lag 2e+05 0.07563581 0.20130185
## Lag 3e+05 0.05627216 0.13138160
## Lag 4e+05 0.04696646 0.09607813
## Lag 5e+05 0.02640095 0.08304586
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.043748960
## Lag 2e+05 0.016336843
## Lag 3e+05 -0.034303463
## Lag 4e+05 0.002055959
## Lag 5e+05 0.025557040
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 -0.0105757303
## Lag 2e+05 0.0081555472
## Lag 3e+05 -0.0008648638
## Lag 4e+05 -0.0211591476
## Lag 5e+05 0.0140501812
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.28853642
## Lag 2e+05 0.15097628
## Lag 3e+05 0.09049351
## Lag 4e+05 0.05343287
## Lag 5e+05 0.02665262
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.31401853 NaN
## Lag 2e+05 0.15580264 NaN
## Lag 3e+05 0.09599644 NaN
## Lag 4e+05 0.06615397 NaN
## Lag 5e+05 0.04150642 NaN
## nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0 NaN 1.000000000 1.0000000000
## Lag 1e+05 NaN 0.007447097 0.0855414617
## Lag 2e+05 NaN 0.019582256 0.0107372129
## Lag 3e+05 NaN -0.010568010 -0.0001462219
## Lag 4e+05 NaN 0.016879791 -0.0146598721
## Lag 5e+05 NaN -0.023449012 0.0133934348
## nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 -0.01042683 -0.004820614 0.03840360
## Lag 2e+05 -0.01252843 -0.013857310 0.04198657
## Lag 3e+05 0.01148737 0.004811532 0.01607645
## Lag 4e+05 -0.00438014 -0.038301198 0.02163643
## Lag 5e+05 0.00657696 -0.021707675 0.01647095
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.21207899 0.27720147 0.26115571
## Lag 2e+05 0.13052454 0.13017927 0.13442124
## Lag 3e+05 0.08384138 0.09694105 0.09832309
## Lag 4e+05 0.07124823 0.08448462 0.06872138
## Lag 5e+05 0.04326489 0.03384386 0.04193409
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.202768955 0.27528665 0.3762952
## Lag 2e+05 0.075770329 0.12129889 0.2364874
## Lag 3e+05 0.038338719 0.05354016 0.1598294
## Lag 4e+05 0.031664424 0.03022846 0.1381709
## Lag 5e+05 0.004408722 0.03849380 0.0907154
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.18890589 0.22853867 0.126792028
## Lag 2e+05 0.07837628 0.08661773 0.024394828
## Lag 3e+05 0.04689467 0.05215740 0.021581356
## Lag 4e+05 0.03514535 0.04500449 0.030138139
## Lag 5e+05 0.02532596 0.03309644 -0.006737163
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.26322366 0.34963847
## Lag 2e+05 0.12805167 0.19680951
## Lag 3e+05 0.09900166 0.14073952
## Lag 4e+05 0.05327681 0.09704682
## Lag 5e+05 0.04357803 0.06100201
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 0.06418533
## Lag 2e+05 0.01204802
## Lag 3e+05 0.03007316
## Lag 4e+05 -0.01015905
## Lag 5e+05 0.01345998
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.003469021
## Lag 2e+05 -0.019135558
## Lag 3e+05 -0.008525964
## Lag 4e+05 0.010091272
## Lag 5e+05 0.005924479
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.30770957
## Lag 2e+05 0.18073830
## Lag 3e+05 0.11857333
## Lag 4e+05 0.08389305
## Lag 5e+05 0.05823593
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.31483963 NaN
## Lag 2e+05 0.16009940 NaN
## Lag 3e+05 0.11984356 NaN
## Lag 4e+05 0.06841642 NaN
## Lag 5e+05 0.04808126 NaN
## nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0 NaN 1.000000000 1.00000000
## Lag 1e+05 NaN 0.003698073 0.12889163
## Lag 2e+05 NaN 0.012692547 0.04062597
## Lag 3e+05 NaN 0.027675974 0.02027939
## Lag 4e+05 NaN -0.022461708 0.01014868
## Lag 5e+05 NaN -0.012870842 0.02027598
## nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.008123602 -0.008209168 0.020872855
## Lag 2e+05 0.017167548 0.013236745 -0.003537093
## Lag 3e+05 0.029501997 -0.009030108 0.006725842
## Lag 4e+05 0.001643124 -0.009019656 0.014109225
## Lag 5e+05 0.037533606 0.005658274 0.001223704
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.24842161 0.30887311 0.26398249
## Lag 2e+05 0.11809260 0.16526930 0.18470749
## Lag 3e+05 0.05698473 0.09648213 0.10306572
## Lag 4e+05 0.06073904 0.08783392 0.09482083
## Lag 5e+05 0.05890767 0.06157450 0.06548860
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.20416669 0.25466160 0.3779820
## Lag 2e+05 0.09348851 0.14472450 0.2121703
## Lag 3e+05 0.05433239 0.07126926 0.1423489
## Lag 4e+05 0.03554781 0.06054000 0.1385701
## Lag 5e+05 0.03359375 0.03396679 0.1051276
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.20900398 0.29614254 0.16677127
## Lag 2e+05 0.09792873 0.14687658 0.06336575
## Lag 3e+05 0.08224240 0.13004955 0.05534394
## Lag 4e+05 0.01918979 0.07404614 0.04040475
## Lag 5e+05 0.01154144 0.05007448 0.01194436
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.196737981 0.32087898
## Lag 2e+05 0.109445949 0.17277115
## Lag 3e+05 0.055800861 0.10563041
## Lag 4e+05 0.005539528 0.05698560
## Lag 5e+05 0.020236940 0.03632713
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 0.08284264
## Lag 2e+05 0.01390810
## Lag 3e+05 0.01166647
## Lag 4e+05 0.02499404
## Lag 5e+05 -0.01585042
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.020707027
## Lag 2e+05 -0.007047753
## Lag 3e+05 -0.002833084
## Lag 4e+05 0.002199343
## Lag 5e+05 -0.022387111
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.32897250
## Lag 2e+05 0.16453049
## Lag 3e+05 0.11725015
## Lag 4e+05 0.07224970
## Lag 5e+05 0.02029036
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.32798588 NaN
## Lag 2e+05 0.18787842 NaN
## Lag 3e+05 0.09916599 NaN
## Lag 4e+05 0.04036421 NaN
## Lag 5e+05 0.03356601 NaN
## nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.030549838 0.092146476
## Lag 2e+05 NaN -0.013837255 0.024044717
## Lag 3e+05 NaN 0.021133086 -0.013529454
## Lag 4e+05 NaN -0.011658025 -0.002811230
## Lag 5e+05 NaN 0.006286412 -0.002560422
## nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.033746441 0.036998231 -0.006527560
## Lag 2e+05 -0.008098698 0.014464298 -0.009669324
## Lag 3e+05 0.016248517 -0.013653410 -0.005182837
## Lag 4e+05 0.009267286 -0.004542760 -0.025725314
## Lag 5e+05 -0.018179530 -0.007545292 0.008523293
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.20980540 0.31258817 0.2705241
## Lag 2e+05 0.10272850 0.18585561 0.1924664
## Lag 3e+05 0.04979621 0.11392135 0.1174185
## Lag 4e+05 0.01319342 0.07599504 0.1102414
## Lag 5e+05 0.03405885 0.07847373 0.0571432
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.16383993 0.26703134 0.3863044
## Lag 2e+05 0.09754358 0.12730479 0.2524307
## Lag 3e+05 0.03840430 0.06587753 0.1999117
## Lag 4e+05 0.01813591 0.03097604 0.1440775
## Lag 5e+05 0.00364068 0.06204510 0.1281311
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.0000000000 1.00000000 1.000000000
## Lag 1e+05 0.1828939335 0.24611213 0.118987747
## Lag 2e+05 0.0795499094 0.15194240 0.066006320
## Lag 3e+05 0.0277291846 0.07713354 0.038746863
## Lag 4e+05 -0.0364320172 0.03012264 -0.002049392
## Lag 5e+05 0.0003647182 0.03000120 -0.001565757
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.21271648 0.32762795
## Lag 2e+05 0.09388585 0.18369446
## Lag 3e+05 0.05741240 0.11909313
## Lag 4e+05 0.03411777 0.08762800
## Lag 5e+05 0.01005790 0.06443821
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0402359137
## Lag 2e+05 0.0335015993
## Lag 3e+05 0.0009190991
## Lag 4e+05 0.0162460178
## Lag 5e+05 -0.0060420610
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.017534063
## Lag 2e+05 0.030580469
## Lag 3e+05 -0.004290301
## Lag 4e+05 0.017491447
## Lag 5e+05 -0.016034924
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.31829795
## Lag 2e+05 0.21754567
## Lag 3e+05 0.13608512
## Lag 4e+05 0.05590445
## Lag 5e+05 0.05483424
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.31007120 NaN
## Lag 2e+05 0.16693452 NaN
## Lag 3e+05 0.11183970 NaN
## Lag 4e+05 0.05391883 NaN
## Lag 5e+05 0.04151056 NaN
## nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.007409601 0.119128549
## Lag 2e+05 NaN -0.005049754 0.045514395
## Lag 3e+05 NaN 0.009320482 0.006768883
## Lag 4e+05 NaN -0.006064997 0.002526589
## Lag 5e+05 NaN 0.024509510 -0.017557989
## nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.009357734 -0.0015865068 0.012673082
## Lag 2e+05 0.009347286 -0.0017059215 -0.011293538
## Lag 3e+05 -0.003961816 0.0006515518 0.010243247
## Lag 4e+05 0.004771873 0.0116216826 -0.005023963
## Lag 5e+05 -0.007313439 -0.0003422546 0.000879657
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.20323118 0.27430986 0.29014167
## Lag 2e+05 0.08033961 0.16289757 0.17577330
## Lag 3e+05 0.06753307 0.10715994 0.09817749
## Lag 4e+05 0.05309927 0.08314507 0.06573527
## Lag 5e+05 0.02179144 0.04947550 0.05646113
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.0000000
## Lag 1e+05 0.188011229 0.25496439 0.3494473
## Lag 2e+05 0.069245254 0.11263445 0.2323210
## Lag 3e+05 0.049888509 0.05222471 0.1772063
## Lag 4e+05 0.024037634 0.04145821 0.1383972
## Lag 5e+05 -0.002349084 0.00173860 0.1153657
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.19230286 0.26014676 0.125499369
## Lag 2e+05 0.08358311 0.12761984 0.041046395
## Lag 3e+05 0.05413363 0.08140981 0.051124703
## Lag 4e+05 0.03766015 0.04240342 0.002972602
## Lag 5e+05 0.01717837 0.02121709 0.005782717
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.20478664 0.33308118
## Lag 2e+05 0.09042746 0.20507179
## Lag 3e+05 0.05870428 0.12017288
## Lag 4e+05 0.02444014 0.04867614
## Lag 5e+05 0.00638726 0.03128094
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.056391924
## Lag 2e+05 -0.016721866
## Lag 3e+05 0.009254921
## Lag 4e+05 -0.013403892
## Lag 5e+05 0.005742263
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 -0.0171879131
## Lag 2e+05 0.0004579701
## Lag 3e+05 0.0023681853
## Lag 4e+05 -0.0021014551
## Lag 5e+05 -0.0066607730
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.30875010
## Lag 2e+05 0.14992387
## Lag 3e+05 0.09106860
## Lag 4e+05 0.05161501
## Lag 5e+05 0.05425171
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O1
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.30513367 NaN
## Lag 2e+05 0.16269995 NaN
## Lag 3e+05 0.08824312 NaN
## Lag 4e+05 0.08624662 NaN
## Lag 5e+05 0.06264501 NaN
## nodefactor.riskg.O2 nodefactor.riskg.O3 nodefactor.riskg.O4
## Lag 0 NaN 1.0000000000 1.00000000
## Lag 1e+05 NaN -0.0025304070 0.10947087
## Lag 2e+05 NaN 0.0003365124 0.03699104
## Lag 3e+05 NaN -0.0365369547 0.01663162
## Lag 4e+05 NaN 0.0005436734 0.01656792
## Lag 5e+05 NaN -0.0127049707 -0.01500576
## nodefactor.riskg.Y1 nodefactor.riskg.Y2 nodefactor.riskg.Y3
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 -0.008687854 0.006495078 0.01134886
## Lag 2e+05 -0.013324973 -0.005250035 0.02062511
## Lag 3e+05 0.020756306 0.014293206 0.01191247
## Lag 4e+05 -0.013087419 -0.022880467 0.01863942
## Lag 5e+05 -0.015383674 -0.012730433 -0.01691345
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.218027320 0.28051393 0.26873391
## Lag 2e+05 0.070311902 0.16076626 0.15729928
## Lag 3e+05 0.061159868 0.09620750 0.09092343
## Lag 4e+05 -0.005816823 0.05425449 0.09236924
## Lag 5e+05 0.005083985 0.02042748 0.07932336
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000e+00 1.00000000 1.00000000
## Lag 1e+05 2.212740e-01 0.26616465 0.37421701
## Lag 2e+05 8.898642e-02 0.16352736 0.25447075
## Lag 3e+05 4.630568e-02 0.11128618 0.17277292
## Lag 4e+05 3.827191e-02 0.07677626 0.11106935
## Lag 5e+05 1.687697e-05 0.06547522 0.05734364
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.170779563 0.25140321 0.110559955
## Lag 2e+05 0.062795813 0.10409109 0.022679230
## Lag 3e+05 0.032425599 0.05422549 0.003049577
## Lag 4e+05 0.008921422 0.01566549 -0.001846504
## Lag 5e+05 0.007504089 0.02137539 0.006181581
##
## Sample statistics burn-in diagnostic (Geweke):## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.35977 -0.25864
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.66514 -0.22960
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.76158 -0.82967
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.02204 0.93421
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## -0.65606 -0.79246
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## -0.63269 0.57698
## nodefactor.race..wa.H nodefactor.region.EW
## 1.37832 0.59875
## nodefactor.region.OW nodematch.race..wa.B
## -1.30730 -0.03470
## nodematch.race..wa.H nodematch.race..wa.O
## 0.48429 -2.10850
## nodematch.region absdiff.sqrt.age
## -0.02003 -0.51141
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.71902169 0.79591663
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.50596233 0.81840590
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.44631274 0.40672803
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.98241491 0.35019558
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## 0.51178421 0.42809134
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## 0.52693610 0.56395282
## nodefactor.race..wa.H nodefactor.region.EW
## 0.16810458 0.54933941
## nodefactor.region.OW nodematch.race..wa.B
## 0.19111149 0.97232263
## nodematch.race..wa.H nodematch.race..wa.O
## 0.62817994 0.03498801
## nodematch.region absdiff.sqrt.age
## 0.98401663 0.60906483
## Joint P-value (lower = worse): 1 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.43175 -2.65103
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.88506 0.91395
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 2.29948 2.22466
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.35977 0.08879
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## -0.39507 -0.34730
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## 1.31724 0.75197
## nodefactor.race..wa.H nodefactor.region.EW
## -0.06923 -1.49686
## nodefactor.region.OW nodematch.race..wa.B
## -0.49181 0.18743
## nodematch.race..wa.H nodematch.race..wa.O
## -1.73754 -0.21735
## nodematch.region absdiff.sqrt.age
## 0.91437 -0.27516
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.665925378 0.008024661
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.059421488 0.360745073
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.021477736 0.026103920
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.719020939 0.929252347
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## 0.692789079 0.728364793
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## 0.187757394 0.452066963
## nodefactor.race..wa.H nodefactor.region.EW
## 0.944803780 0.134429514
## nodefactor.region.OW nodematch.race..wa.B
## 0.622851387 0.851321222
## nodematch.race..wa.H nodematch.race..wa.O
## 0.082291377 0.827939139
## nodematch.region absdiff.sqrt.age
## 0.360522179 0.783191955
## Joint P-value (lower = worse): 0.002602775 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.497347 1.104568
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.114861 -0.241551
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.410172 -0.491445
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -0.308175 -0.248053
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## 0.757081 -1.391366
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## -0.035174 -1.854815
## nodefactor.race..wa.H nodefactor.region.EW
## -0.062057 0.904253
## nodefactor.region.OW nodematch.race..wa.B
## 0.004068 -2.323121
## nodematch.race..wa.H nodematch.race..wa.O
## 0.733962 1.552451
## nodematch.region absdiff.sqrt.age
## 0.130896 0.554196
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.61894443 0.26934670
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.90855496 0.80912818
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.68168008 0.62311201
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.75794905 0.80409350
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## 0.44900157 0.16411441
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## 0.97194123 0.06362269
## nodefactor.race..wa.H nodefactor.region.EW
## 0.95051783 0.36586122
## nodefactor.region.OW nodematch.race..wa.B
## 0.99675394 0.02017267
## nodematch.race..wa.H nodematch.race..wa.O
## 0.46297174 0.12055445
## nodematch.region absdiff.sqrt.age
## 0.89585735 0.57944457
## Joint P-value (lower = worse): 0.5778305 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.61163 -1.83653
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.91592 0.05771
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.96381 0.65141
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -0.90643 -1.29679
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## -0.25577 1.53631
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## -0.92837 -1.04431
## nodefactor.race..wa.H nodefactor.region.EW
## 0.71199 -0.96415
## nodefactor.region.OW nodematch.race..wa.B
## -1.37408 -0.72397
## nodematch.race..wa.H nodematch.race..wa.O
## 1.59071 -0.36997
## nodematch.region absdiff.sqrt.age
## -0.11652 -0.61764
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.54078181 0.06627883
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.35970969 0.95398007
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.04955223 0.51477965
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.36470846 0.19470412
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## 0.79812986 0.12446347
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## 0.35321773 0.29633998
## nodefactor.race..wa.H nodefactor.region.EW
## 0.47646820 0.33497154
## nodefactor.region.OW nodematch.race..wa.B
## 0.16941726 0.46908624
## nodematch.race..wa.H nodematch.race..wa.O
## 0.11167516 0.71140619
## nodematch.region absdiff.sqrt.age
## 0.90723921 0.53681079
## Joint P-value (lower = worse): 0.07251787 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.36609 1.40760
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -2.76873 -0.00147
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.43890 0.05609
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 2.12012 -0.86632
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## 0.81795 -0.39212
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## -0.84273 1.55267
## nodefactor.race..wa.H nodefactor.region.EW
## -0.30832 -1.17266
## nodefactor.region.OW nodematch.race..wa.B
## 0.18378 -0.65369
## nodematch.race..wa.H nodematch.race..wa.O
## 0.08796 1.62517
## nodematch.region absdiff.sqrt.age
## 2.19649 1.32998
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.171910051 0.159249243
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.005627449 0.998827089
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.660735069 0.955272608
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.033996241 0.386314680
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## 0.413386808 0.694973235
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## 0.399377086 0.120502789
## nodefactor.race..wa.H nodefactor.region.EW
## 0.757841196 0.240933804
## nodefactor.region.OW nodematch.race..wa.B
## 0.854189144 0.513309588
## nodematch.race..wa.H nodematch.race..wa.O
## 0.929911978 0.104126997
## nodematch.region absdiff.sqrt.age
## 0.028056952 0.183523699
## Joint P-value (lower = worse): 1.99522e-05 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.45346 0.21544
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.79415 -0.09743
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.23924 0.63003
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.44566 1.51245
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## -0.66797 -1.45091
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## -0.64993 -0.78657
## nodefactor.race..wa.H nodefactor.region.EW
## -1.11427 -0.77462
## nodefactor.region.OW nodematch.race..wa.B
## 0.20048 -0.46746
## nodematch.race..wa.H nodematch.race..wa.O
## -0.13284 1.74283
## nodematch.region absdiff.sqrt.age
## 0.87655 1.30865
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.65021697 0.82942345
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.42710750 0.92238196
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.81091897 0.52867294
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.65584158 0.13042032
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## 0.50415196 0.14680546
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## 0.51573466 0.43153098
## nodefactor.race..wa.H nodefactor.region.EW
## 0.26516408 0.43856429
## nodefactor.region.OW nodematch.race..wa.B
## 0.84110352 0.64017201
## nodematch.race..wa.H nodematch.race..wa.O
## 0.89432123 0.08136331
## nodematch.region absdiff.sqrt.age
## 0.38073137 0.19065171
## Joint P-value (lower = worse): 0.2812042 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.76164 0.82730
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.55628 -1.19049
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.63246 -1.12443
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## -0.12668 1.50833
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## -0.91231 0.50026
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## 0.54102 -0.47719
## nodefactor.race..wa.H nodefactor.region.EW
## 0.06583 1.62955
## nodefactor.region.OW nodematch.race..wa.B
## -1.01388 -0.21770
## nodematch.race..wa.H nodematch.race..wa.O
## 0.47254 1.41034
## nodematch.region absdiff.sqrt.age
## 1.13479 0.60257
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.4462766 0.4080698
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.5780168 0.2338554
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.1025833 0.2608316
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.8991958 0.1314706
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## 0.3616077 0.6168903
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## 0.5884905 0.6332281
## nodefactor.race..wa.H nodefactor.region.EW
## 0.9475156 0.1031972
## nodefactor.region.OW nodematch.race..wa.B
## 0.3106410 0.8276630
## nodematch.race..wa.H nodematch.race..wa.O
## 0.6365385 0.1584385
## nodematch.region absdiff.sqrt.age
## 0.2564653 0.5467969
## Joint P-value (lower = worse): 0.3569499 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.32005 0.16278
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.10403 -1.28204
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.02223 -0.14860
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 0.00000 0.16310
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## 0.06042 -0.22094
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## -3.09206 -1.33016
## nodefactor.race..wa.H nodefactor.region.EW
## 0.31502 0.95794
## nodefactor.region.OW nodematch.race..wa.B
## 0.68062 -1.03498
## nodematch.race..wa.H nodematch.race..wa.O
## 0.99788 0.70932
## nodematch.region absdiff.sqrt.age
## -0.02342 0.01909
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.74892678 0.87068912
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.91714746 0.19982780
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.98226497 0.88186994
## nodefactor.riskg.O1 nodefactor.riskg.O2
## NaN NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4
## 1.00000000 0.87044004
## nodefactor.riskg.Y1 nodefactor.riskg.Y2
## 0.95182405 0.82514157
## nodefactor.riskg.Y3 nodefactor.race..wa.B
## 0.00198773 0.18346524
## nodefactor.race..wa.H nodefactor.region.EW
## 0.75274979 0.33809178
## nodefactor.region.OW nodematch.race..wa.B
## 0.49611236 0.30067984
## nodematch.race..wa.H nodematch.race..wa.O
## 0.31833748 0.47812722
## nodematch.region absdiff.sqrt.age
## 0.98131516 0.98477074
## Joint P-value (lower = worse): 0.006832838 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Summary of model fit
Model 1
summary(est.i.buildup.bal[[1]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55bdbcf8b970>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -11.48530 0.04581 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 2
summary(est.i.buildup.bal[[2]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55bdd82cae28>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -11.65068 0.05800 0 < 1e-04 ***
## nodefactor.race..wa.B 0.34267 0.12152 0 0.00481 **
## nodefactor.race..wa.H 0.44886 0.08959 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 3
summary(est.i.buildup.bal[[3]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa",
## diff = TRUE) + offset(nodematch("role.class", diff = TRUE,
## keep = 1:2))
## <environment: 0x55bdee0f1e08>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -12.1436 0.2967 0 < 1e-04 ***
## nodefactor.race..wa.B 0.7643 0.2648 0 0.00390 **
## nodefactor.race..wa.H 0.8665 0.2812 0 0.00206 **
## nodematch.race..wa.B -0.5122 0.6963 0 0.46191
## nodematch.race..wa.H -0.2075 0.4094 0 0.61225
## nodematch.race..wa.O 0.5148 0.3024 0 0.08872 .
## nodematch.role.class.I -Inf 0.0000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.0000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 4
summary(est.i.buildup.bal[[4]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa",
## base = 3) + nodematch("race..wa", diff = TRUE) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55be041a57e0>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -11.94315 0.30323 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1 0.89117 0.08973 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -0.80008 0.17229 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0 -2.22091 0.16865 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 0.74878 0.09862 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 0.78870 0.09545 0 < 1e-04 ***
## nodefactor.race..wa.B 0.72925 0.26123 0 0.00524 **
## nodefactor.race..wa.H 0.90359 0.27964 0 0.00123 **
## nodematch.race..wa.B -0.51085 0.68486 0 0.45572
## nodematch.race..wa.H -0.20830 0.40524 0 0.60723
## nodematch.race..wa.O 0.51492 0.30096 0 0.08709 .
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 5
summary(est.i.buildup.bal[[5]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + offset(nodematch("role.class", diff = TRUE,
## keep = 1:2))
## <environment: 0x55be1a4ef778>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -11.59388 0.30997 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1 0.89312 0.09046 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -0.81131 0.17350 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0 -2.25532 0.17056 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 0.70708 0.09916 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 0.75409 0.09641 0 < 1e-04 ***
## nodefactor.race..wa.B 0.68515 0.26507 0 0.009743 **
## nodefactor.race..wa.H 0.94696 0.28314 0 0.000824 ***
## nodefactor.region.EW -0.39073 0.11825 0 0.000952 ***
## nodefactor.region.OW -0.43996 0.07620 0 < 1e-04 ***
## nodematch.race..wa.B -0.51373 0.68614 0 0.454026
## nodematch.race..wa.H -0.21283 0.40358 0 0.597951
## nodematch.race..wa.O 0.51908 0.30414 0 0.087872 .
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 6
summary(est.i.buildup.bal[[6]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55be3094b810>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.98360 0.31398 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1 0.89776 0.08986 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -0.81143 0.17262 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0 -2.25785 0.16968 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 0.71330 0.09909 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 0.75700 0.09615 0 < 1e-04 ***
## nodefactor.race..wa.B 0.67961 0.26428 0 0.010123 *
## nodefactor.race..wa.H 0.94895 0.28142 0 0.000746 ***
## nodefactor.region.EW -0.39309 0.11725 0 0.000801 ***
## nodefactor.region.OW -0.43980 0.07551 0 < 1e-04 ***
## nodematch.race..wa.B -0.51131 0.69248 0 0.460282
## nodematch.race..wa.H -0.20997 0.40483 0 0.603993
## nodematch.race..wa.O 0.51619 0.30258 0 0.088017 .
## absdiff.sqrt.age -0.63705 0.06875 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 7
summary(est.i.buildup.bal[[7]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("riskg") +
## nodefactor("race..wa", base = 3) + nodefactor("region", base = 2) +
## nodematch("race..wa", diff = TRUE) + absdiff("sqrt.age") +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55be4d0f8ec8>
##
## Iterations: 3 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -6.39857 0.30979 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1 0.95094 0.09062 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -0.91826 0.17278 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0 -2.36303 0.17068 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 0.71388 0.09814 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 0.95482 0.09756 0 < 1e-04 ***
## nodefactor.riskg.O2 -18.81234 NA NA NA
## nodefactor.riskg.O3 -18.78460 NA NA NA
## nodefactor.riskg.O4 -4.81294 0.38712 0 < 1e-04 ***
## nodefactor.riskg.Y1 -3.57404 0.14262 0 < 1e-04 ***
## nodefactor.riskg.Y2 -7.95999 0.86162 0 < 1e-04 ***
## nodefactor.riskg.Y3 -6.17949 0.36565 0 < 1e-04 ***
## nodefactor.riskg.Y4 -4.03144 0.15921 0 < 1e-04 ***
## nodefactor.race..wa.B 1.20301 0.26386 0 < 1e-04 ***
## nodefactor.race..wa.H 1.10908 0.27895 0 < 1e-04 ***
## nodefactor.region.EW -0.28311 0.11867 0 0.017051 *
## nodefactor.region.OW -0.51623 0.07621 0 < 1e-04 ***
## nodematch.race..wa.B -0.51222 0.69673 0 0.462235
## nodematch.race..wa.H -0.19558 0.40117 0 0.625891
## nodematch.race..wa.O 0.53630 0.30210 0 0.075859 .
## absdiff.sqrt.age 0.26781 0.07545 0 0.000386 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 8
summary(est.i.buildup.bal[[8]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("riskg",
## base = 8) + nodefactor("race..wa", base = 3) + nodefactor("region",
## base = 2) + nodematch("race..wa", diff = TRUE) + nodematch("region",
## diff = FALSE) + absdiff("sqrt.age") + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55be670ea678>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -9.69942 0.33331 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.1 0.98692 0.08954 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -0.74837 0.17207 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.0 -2.22953 0.16949 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 0.80517 0.10004 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 0.76315 0.09568 0 < 1e-04 ***
## nodefactor.riskg.O1 -17.19841 NA NA NA
## nodefactor.riskg.O2 -17.29306 NA NA NA
## nodefactor.riskg.O3 -3.34355 0.37048 0 < 1e-04 ***
## nodefactor.riskg.O4 -0.52291 0.09667 0 < 1e-04 ***
## nodefactor.riskg.Y1 -6.34413 0.84913 0 < 1e-04 ***
## nodefactor.riskg.Y2 -4.53215 0.35023 0 < 1e-04 ***
## nodefactor.riskg.Y3 -2.40631 0.12476 0 < 1e-04 ***
## nodefactor.race..wa.B 0.51364 0.26614 0 0.05361 .
## nodefactor.race..wa.H 0.90836 0.28387 0 0.00137 **
## nodefactor.region.EW 0.17206 0.10230 0 0.09258 .
## nodefactor.region.OW -0.11012 0.06073 0 0.06978 .
## nodematch.race..wa.B -0.42303 0.57415 0 0.46124
## nodematch.race..wa.H -0.26994 0.40518 0 0.50527
## nodematch.race..wa.O 0.54402 0.30539 0 0.07485 .
## nodematch.region 1.74468 0.11978 0 < 1e-04 ***
## absdiff.sqrt.age -0.58753 0.07097 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfNetwork diagnostics
Model 1
(dx_inst1 <- netdx(est.i.buildup.bal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 479.015 0 21.817
## nodefactor.deg.main.deg.pers.0.1 NA 63.905 NA 8.248
## nodefactor.deg.main.deg.pers.0.2 NA 73.868 NA 8.839
## nodefactor.deg.main.deg.pers.1.0 NA 312.773 NA 20.538
## nodefactor.deg.main.deg.pers.1.1 NA 57.717 NA 7.752
## nodefactor.deg.main.deg.pers.1.2 NA 58.685 NA 7.919
## nodefactor.riskg.O1 NA 54.780 NA 7.567
## nodefactor.riskg.O2 NA 54.490 NA 7.637
## nodefactor.riskg.O3 NA 55.027 NA 7.663
## nodefactor.riskg.O4 NA 54.761 NA 7.647
## nodefactor.riskg.Y1 NA 184.416 NA 14.698
## nodefactor.riskg.Y2 NA 184.435 NA 14.785
## nodefactor.riskg.Y3 NA 184.883 NA 15.022
## nodefactor.race..wa.B NA 58.125 NA 7.840
## nodefactor.race..wa.H NA 103.389 NA 10.759
## nodefactor.region.EW NA 96.802 NA 10.309
## nodefactor.region.OW NA 313.245 NA 20.235
## nodematch.race..wa.B NA 1.774 NA 1.343
## nodematch.race..wa.H NA 5.570 NA 2.387
## nodematch.race..wa.O NA 331.161 NA 18.350
## nodematch.region NA 212.770 NA 14.471
## absdiff.sqrt.age NA 547.666 NA 30.597
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst1, type="formation")
plot(dx_inst1, type="duration")
plot(dx_inst1, type="dissolution")
Model 2
(dx_inst2 <- netdx(est.i.buildup.bal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 479.033 0.000 21.914
## nodefactor.deg.main.deg.pers.0.1 NA 63.842 NA 8.277
## nodefactor.deg.main.deg.pers.0.2 NA 72.970 NA 9.006
## nodefactor.deg.main.deg.pers.1.0 NA 315.375 NA 20.467
## nodefactor.deg.main.deg.pers.1.1 NA 57.628 NA 7.791
## nodefactor.deg.main.deg.pers.1.2 NA 59.589 NA 7.954
## nodefactor.riskg.O1 NA 54.605 NA 7.587
## nodefactor.riskg.O2 NA 54.485 NA 7.699
## nodefactor.riskg.O3 NA 54.880 NA 7.702
## nodefactor.riskg.O4 NA 55.430 NA 7.656
## nodefactor.riskg.Y1 NA 185.282 NA 14.928
## nodefactor.riskg.Y2 NA 183.891 NA 14.863
## nodefactor.riskg.Y3 NA 185.000 NA 14.831
## nodefactor.race..wa.B 75.591 75.789 0.003 9.040
## nodefactor.race..wa.H 149.174 149.332 0.001 13.165
## nodefactor.region.EW NA 100.611 NA 10.622
## nodefactor.region.OW NA 309.917 NA 20.284
## nodematch.race..wa.B NA 3.001 NA 1.729
## nodematch.race..wa.H NA 11.634 NA 3.483
## nodematch.race..wa.O NA 280.387 NA 16.720
## nodematch.region NA 211.892 NA 14.469
## absdiff.sqrt.age NA 547.260 NA 30.689
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst2, type="formation")
plot(dx_inst2, type="duration")
plot(dx_inst2, type="dissolution")
Model 3
(dx_inst3 <- netdx(est.i.buildup.bal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 478.979 0.000 21.831
## nodefactor.deg.main.deg.pers.0.1 NA 63.997 NA 8.249
## nodefactor.deg.main.deg.pers.0.2 NA 73.099 NA 8.947
## nodefactor.deg.main.deg.pers.1.0 NA 315.262 NA 20.178
## nodefactor.deg.main.deg.pers.1.1 NA 57.640 NA 7.838
## nodefactor.deg.main.deg.pers.1.2 NA 59.599 NA 7.914
## nodefactor.riskg.O1 NA 54.506 NA 7.583
## nodefactor.riskg.O2 NA 54.413 NA 7.534
## nodefactor.riskg.O3 NA 54.860 NA 7.655
## nodefactor.riskg.O4 NA 55.426 NA 7.739
## nodefactor.riskg.Y1 NA 185.198 NA 14.778
## nodefactor.riskg.Y2 NA 183.714 NA 14.556
## nodefactor.riskg.Y3 NA 185.269 NA 14.852
## nodefactor.race..wa.B 75.591 75.512 -0.001 9.134
## nodefactor.race..wa.H 149.174 149.098 -0.001 13.200
## nodefactor.region.EW NA 100.762 NA 10.538
## nodefactor.region.OW NA 309.919 NA 20.129
## nodematch.race..wa.B 2.540 2.524 -0.006 1.583
## nodematch.race..wa.H 13.269 13.301 0.002 3.650
## nodematch.race..wa.O 286.880 286.773 0.000 16.954
## nodematch.region NA 211.646 NA 14.672
## absdiff.sqrt.age NA 547.557 NA 30.698
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst3, type="formation")
plot(dx_inst3, type="duration")
plot(dx_inst3, type="dissolution")
Model 4
(dx_inst4 <- netdx(est.i.buildup.bal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 478.971 0.000 22.061
## nodefactor.deg.main.deg.pers.0.1 172.310 172.246 0.000 14.255
## nodefactor.deg.main.deg.pers.0.2 36.371 36.415 0.001 6.110
## nodefactor.deg.main.deg.pers.1.0 38.033 38.020 0.000 6.297
## nodefactor.deg.main.deg.pers.1.1 135.538 135.422 -0.001 12.513
## nodefactor.deg.main.deg.pers.1.2 145.388 145.149 -0.002 12.863
## nodefactor.riskg.O1 NA 56.315 NA 7.608
## nodefactor.riskg.O2 NA 55.056 NA 7.717
## nodefactor.riskg.O3 NA 55.828 NA 7.606
## nodefactor.riskg.O4 NA 55.470 NA 7.675
## nodefactor.riskg.Y1 NA 188.548 NA 14.925
## nodefactor.riskg.Y2 NA 181.348 NA 14.737
## nodefactor.riskg.Y3 NA 178.944 NA 14.599
## nodefactor.race..wa.B 75.591 75.536 -0.001 8.877
## nodefactor.race..wa.H 149.174 148.884 -0.002 13.296
## nodefactor.region.EW NA 103.579 NA 10.682
## nodefactor.region.OW NA 317.762 NA 20.744
## nodematch.race..wa.B 2.540 2.510 -0.012 1.551
## nodematch.race..wa.H 13.269 13.263 0.000 3.668
## nodematch.race..wa.O 286.880 286.938 0.000 17.095
## nodematch.region NA 208.381 NA 14.491
## absdiff.sqrt.age NA 550.698 NA 30.909
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst4, type="formation")
plot(dx_inst4, type="duration")
plot(dx_inst4, type="dissolution")
Model 5
(dx_inst5 <- netdx(est.i.buildup.bal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 479.226 0.000 21.865
## nodefactor.deg.main.deg.pers.0.1 172.310 172.284 0.000 14.375
## nodefactor.deg.main.deg.pers.0.2 36.371 36.335 -0.001 6.196
## nodefactor.deg.main.deg.pers.1.0 38.033 38.049 0.000 6.273
## nodefactor.deg.main.deg.pers.1.1 135.538 135.660 0.001 12.413
## nodefactor.deg.main.deg.pers.1.2 145.388 145.092 -0.002 13.129
## nodefactor.riskg.O1 NA 57.219 NA 7.756
## nodefactor.riskg.O2 NA 55.557 NA 7.698
## nodefactor.riskg.O3 NA 56.063 NA 7.811
## nodefactor.riskg.O4 NA 54.728 NA 7.668
## nodefactor.riskg.Y1 NA 187.212 NA 14.888
## nodefactor.riskg.Y2 NA 182.947 NA 14.702
## nodefactor.riskg.Y3 NA 178.344 NA 14.423
## nodefactor.race..wa.B 75.591 75.533 -0.001 8.941
## nodefactor.race..wa.H 149.174 149.136 0.000 13.425
## nodefactor.region.EW 83.501 83.440 -0.001 9.630
## nodefactor.region.OW 242.486 242.575 0.000 17.163
## nodematch.race..wa.B 2.540 2.540 0.000 1.597
## nodematch.race..wa.H 13.269 13.265 0.000 3.672
## nodematch.race..wa.O 286.880 287.020 0.000 16.926
## nodematch.region NA 242.945 NA 15.656
## absdiff.sqrt.age NA 551.233 NA 30.994
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst5, type="formation")
plot(dx_inst5, type="duration")
plot(dx_inst5, type="dissolution")
Model 6
(dx_inst6 <- netdx(est.i.buildup.bal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 478.735 -0.001 21.832
## nodefactor.deg.main.deg.pers.0.1 172.310 172.227 0.000 14.199
## nodefactor.deg.main.deg.pers.0.2 36.371 36.433 0.002 6.166
## nodefactor.deg.main.deg.pers.1.0 38.033 37.929 -0.003 6.318
## nodefactor.deg.main.deg.pers.1.1 135.538 135.411 -0.001 12.315
## nodefactor.deg.main.deg.pers.1.2 145.388 145.204 -0.001 12.912
## nodefactor.riskg.O1 NA 52.898 NA 7.572
## nodefactor.riskg.O2 NA 51.588 NA 7.583
## nodefactor.riskg.O3 NA 52.599 NA 7.670
## nodefactor.riskg.O4 NA 51.127 NA 7.483
## nodefactor.riskg.Y1 NA 190.007 NA 15.087
## nodefactor.riskg.Y2 NA 187.080 NA 14.936
## nodefactor.riskg.Y3 NA 181.883 NA 14.776
## nodefactor.race..wa.B 75.591 75.437 -0.002 9.048
## nodefactor.race..wa.H 149.174 149.162 0.000 13.259
## nodefactor.region.EW 83.501 83.406 -0.001 9.491
## nodefactor.region.OW 242.486 242.423 0.000 17.565
## nodematch.race..wa.B 2.540 2.536 -0.002 1.579
## nodematch.race..wa.H 13.269 13.292 0.002 3.637
## nodematch.race..wa.O 286.880 286.565 -0.001 16.870
## nodematch.region NA 242.486 NA 15.608
## absdiff.sqrt.age 380.500 380.491 0.000 22.595
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst6, type="formation")
plot(dx_inst6, type="duration")
plot(dx_inst6, type="dissolution")
Model 7
(dx_inst7 <- netdx(est.i.buildup.bal[[7]], nsims = 10, nsteps = 1000, ncores = 4, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 240.146 -0.499 15.423
## nodefactor.deg.main.deg.pers.0.1 172.310 74.549 -0.567 9.323
## nodefactor.deg.main.deg.pers.0.2 36.371 23.953 -0.341 4.991
## nodefactor.deg.main.deg.pers.1.0 38.033 34.338 -0.097 6.017
## nodefactor.deg.main.deg.pers.1.1 135.538 57.343 -0.577 7.922
## nodefactor.deg.main.deg.pers.1.2 145.388 62.023 -0.573 8.397
## nodefactor.riskg.O2 0.401 0.000 -1.000 0.000
## nodefactor.riskg.O3 0.401 0.000 -1.000 0.000
## nodefactor.riskg.O4 6.856 6.884 0.004 2.633
## nodefactor.riskg.Y1 109.513 98.987 -0.096 10.354
## nodefactor.riskg.Y2 1.349 1.345 -0.003 1.161
## nodefactor.riskg.Y3 8.202 8.189 -0.002 2.830
## nodefactor.riskg.Y4 70.786 67.042 -0.053 8.296
## nodefactor.race..wa.B 75.591 39.151 -0.482 6.422
## nodefactor.race..wa.H 149.174 66.343 -0.555 8.651
## nodefactor.region.EW 83.501 43.250 -0.482 6.823
## nodefactor.region.OW 242.486 131.260 -0.459 12.845
## nodematch.race..wa.B 2.540 1.413 -0.444 1.191
## nodematch.race..wa.H 13.269 5.067 -0.618 2.227
## nodematch.race..wa.O 286.880 148.018 -0.484 12.155
## absdiff.sqrt.age 380.500 301.563 -0.207 24.945
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst7, type="formation")
plot(dx_inst7, type="duration")
plot(dx_inst7, type="dissolution")
Model 8
(dx_inst8 <- netdx(est.i.buildup.bal[[8]], nsims = 10, nsteps = 1000, ncores = 4, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+5, MCMC.burnin.max = 1e+5)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 479.159 382.651 -0.201 19.588
## nodefactor.deg.main.deg.pers.0.1 172.310 120.857 -0.299 11.710
## nodefactor.deg.main.deg.pers.0.2 36.371 34.694 -0.046 6.006
## nodefactor.deg.main.deg.pers.1.0 38.033 38.189 0.004 6.302
## nodefactor.deg.main.deg.pers.1.1 135.538 94.401 -0.304 10.191
## nodefactor.deg.main.deg.pers.1.2 145.388 103.699 -0.287 10.866
## nodefactor.riskg.O1 0.401 0.000 -1.000 0.000
## nodefactor.riskg.O2 0.401 0.000 -1.000 0.010
## nodefactor.riskg.O3 6.856 6.871 0.002 2.631
## nodefactor.riskg.O4 109.513 97.724 -0.108 11.039
## nodefactor.riskg.Y1 1.349 1.368 0.014 1.172
## nodefactor.riskg.Y2 8.202 8.130 -0.009 2.874
## nodefactor.riskg.Y3 70.786 70.442 -0.005 8.709
## nodefactor.race..wa.B 75.591 60.147 -0.204 8.125
## nodefactor.race..wa.H 149.174 108.233 -0.274 11.155
## nodefactor.region.EW 83.501 66.050 -0.209 9.606
## nodefactor.region.OW 242.486 204.241 -0.158 18.745
## nodematch.race..wa.B 2.540 2.326 -0.084 1.526
## nodematch.race..wa.H 13.269 8.341 -0.371 2.879
## nodematch.race..wa.O 286.880 235.903 -0.178 15.499
## nodematch.region 383.327 289.268 -0.245 17.058
## absdiff.sqrt.age 380.500 323.774 -0.149 21.072
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst8, type="formation")
plot(dx_inst8, type="duration")
plot(dx_inst8, type="dissolution")